AIRWiki - User contributions [en]

Pajek graph data format

2011-03-16T11:48:55Z

RiccardoTasso:

== Example ==
<code>
<nowiki>
*Vertices 4
1 "uno"
2 "due"
3 "tre"
4 "quattro"

*Edges
1 2
2 3
3 4
</nowiki>
</code>

== References ==
* http://vw.indiana.edu/tutorials/pajek/
* http://iv.slis.indiana.edu/lm/lm-pajek.html

Pajek graph data format

2011-03-16T11:47:24Z

RiccardoTasso: New page: == Example == <code> *Vertices 4 1 "uno" 2 "due" 3 "tre" 4 "quattro" *Edges 1 2 2 3 3 4 </code> == References == * http://vw.indiana.edu/tutorials/pajek/ * http://iv.slis.indiana.edu/lm/...

== Example ==
<code>
*Vertices 4
1 "uno"
2 "due"
3 "tre"
4 "quattro"

*Edges
1 2
2 3
3 4
</code>

== References ==
* http://vw.indiana.edu/tutorials/pajek/
* http://iv.slis.indiana.edu/lm/lm-pajek.html

Social Network Analysis With Igraph Package Using R

2011-03-16T11:43:35Z

RiccardoTasso:

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', [[pajek graph data format|format='pajek']])</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
'''graph size''' (number of nodes): length(V(g))

'''number of edges''': length(E(g))

'''diameter''': diameter(g)

'''mean degree''':

'''density''':

'''giant component size''' (the largest connected component size):

assortativity:

== Node Centrality Analysis ==

'''degree''': degree(g)

'''closeness''': closeness(g)

'''betweenness''': betweenness(g)

'''eigenvector'''/'''pagerank''': evcent(g)

== Clustering Techniques ==

'''connected components''': clusters(g)

'''edge betweenness''':

'''walktrap''':

'''spinglass''':

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

:Student

2010-11-15T11:42:57Z

RiccardoTasso:

<noinclude>
This is the 'Student' form.
To add a page with this form, enter the page name below;
if a page with that name already exists, you will be sent to a form to edit that page.

{{#forminput:Student}}

</noinclude><includeonly>
{{{for template|Student}}}
{| class="formtable"
! Category:
| {{{field|category|mandatory|values=Student,PhD,Researcher,Prof|default=Student}}}
|-
! Firstname:
| {{{field|firstname|mandatory}}}
|-
! Lastname:
| {{{field|lastname|mandatory}}}
|-
! Photo:
| {{{field|photo|uploadable}}}
|-
! Email:
| {{{field|email|mandatory}}}
|-
! Projectpage:
| {{{field|projectpage}}}
|-
! [[Property:HasAdvisor|Advisor(s)]]:
| {{{field|advisor|autocomplete on category=Tutor|delimiter=; }}}
|-
! [[Property:UserStatus|Status]]:
| {{{field|status|dropdown}}}
|}
{{{end template}}}

'''Free text:'''

{{{standard input|free text}}}

{{{standard input|minor edit}}} {{{standard input|watch}}}

{{{standard input|save}}} {{{standard input|preview}}} {{{standard input|changes}}} {{{standard input|cancel}}}
</includeonly>

A tool to find and store annotations into a unifying semantic ontology

2010-11-02T12:02:57Z

RiccardoTasso:

{{Project
|title=A tool to find and store annotations into a unifying semantic ontology
|coordinator=DavideEynard
|tutor=DavideEynard; DavidLaniado; RiccardoTasso;
|students=StefanoInvernizzi; RiccardoMocchetti
|resarea=Social Software and Semantic Web
|restopic=Semantic Annotations
|start=2010/03/26
|end=2010/09/27
|status=Closed
|level=Bs
|type=Thesis
|report=[[Image:AIRLabReport.pdf‎]]
}}
[[Category:Project]]

==Description==
The aim of the project is to acquire annotations about resources (identifyed by an URI) from existing systems (e.g.: StumbleUpon, del.icio.us, Wikies, ...) and to store them into a unifying ontology.

For details about the adopted general model for semantic annotations, see ''[[A Unifying Framework for Semantic Annotation]]''.

Wikipedia Category Graph

2010-10-11T13:27:36Z

RiccardoTasso: /* Download */

{{Project
|title=Automatically assigning Wikipedia articles to macrocategories
|short_descr=Represent Wikipedia Categories with a model based on graphs to further analyze it.
|coordinator=MarcoColombetti
|tutor=DavidLaniado;RiccardoTasso
|students=JacopoFarina;
|resarea=Social Software and Semantic Web
|restopic=Graph Mining and Analysis; Semantic Tagging;
|start=2010/06/10
|end=2010/10/01
|status=Closed
|level=Bs
|type=Thesis
}}
The goal of the project is to analyze Wikipedia categories by representing them in a graph based database.

Wikipedia categories are not a tree-based structure: a category may be contained in another one which is contained in another one which is contained in the first, generating a cyclic reference and many categories may be a root category (non contained in others).

For thess reasons a graph database is better to represent the structure.
==Creation and further analysis of the database with igraph==
Wikipedia lets users download the entire site database (with all versions of all articles) or just some selections of it.
We use a selection which contains the category structure and article memberships in them.

[http://neo4j.org/ Neo4j] is a graph-based database, which allow a program to create and manipulate graph structures like nodes and relationships.

In order to transfer the database in neo4j format is better save it in a file, which will be read one line at time.

After transferring the structure in a Neo4j graph it is possible to create from it a Pajek file (.net) to make general analysis like described [[Social_Network_Analysis_With_Igraph_Package_Using_R|here]].

==Results of the analysis==
We can use the tools described [http://igraph.sourceforge.net/doc/R/00Index.html here]

The '''diameter''' of the graph is '''32'''. This is the maximum distance (number of nodes in the minimum path) between two nodes. These two nodes are ''Prehistoric life sorted by geography'' (A category about prehistoric animals without articles) and ''BMW M20'' (a car).

The '''average distance''' between two nodes is '''5.5568'''.

The '''graph density''' is '''6.28*10-7'''. This is the ratio of the number of edges and the number of possible edges.

By analysing the graph with little Java programs written for this task we calculated the average number of categories per article is '''2.68'''.

Statistically, the 93% of articles has less than 7 categories, 64% less than 3.

The article with more categories is [http://en.wikipedia.org/wiki/Winston_Churchill Winston Churchill], with 70 categories. [http://en.wikipedia.org/wiki/Albert_einstein Albert Einstein] has the notable number of 56 categories, too.

===Strongly connected components===

By applying the Tarjan's strongly connected components algorithm to the graph is possible to found 93 structures of up to 2 nodes. Each of them contains at least one cycle. Most of them are composed of two categories about the same thing, like ''History of the Germanic peoples'' and ''Ancient Germanic peoples'', but there are also more curious cases like this one
[[Image:Struttura fortemente connessa wikipedia.png|400px]]

=== Tested algorithms ===
We tried 9 algorithms to choose the category which fit best an article. After confronting the results of the automatic procedure with human made assignments, the best algorithm was choose a different weight to each edge by the traversal direction.
Sizes of the macrocategories determined this way are:
[[Image:Dimensioni macrocategorie costi.png|400px]]

==Download==
*The relation about the project (in Italian) can be found [[Media:Relazione progetto WikipediaCatGraph.pdf|here]]
*Sources in Java can be found [[Media:Wikipedia category Graph-sources.zip|here]]
*Slides of the thesis (in Italian) [[Media:Presentazione wikipedia category graph.pdf|here]]
*The thesis (in Italian) [[Media:Tesi wikipedia category graph.pdf|here]]

==Previous Work==

*[http://www-users.cs.umn.edu/~echi/papers/2009-CHI2009/p1509.pdf What's in Wikipedia? Mapping Topics and Conflict Using Socially]

Template:Researcher

2010-09-29T16:46:17Z

RiccardoTasso: Reverted edits by RiccardoTasso (Talk) to last version by DavidLaniado

<noinclude>This template simplifies the creation of articles about researchers. When inserted in a person's page, it creates a decorative table with much helpful information. It also takes care of annotating the given data semantically, so that users can easily find it or query it in other articles. Do not try to read this page's source code for learning table syntax – there are far easier ways of creating tables in MediaWiki.

To use this template, insert the following at the beginning of your user page. All fields are strictly '''optional''' and should be omitted if no data is given.

'''Please delete all lines that you do not give useful information for!'''
<pre>{{Researcher
| firstname=Mario
| lastname=Rossi
| email=mario.rossi@elet.polimi.it
| resarea=The name of your research area
| ***NOTE:*** you can specify a LIST of research areas, separating them with a semicolon (;)
| photo=name of picture (e.g. "Mario.jpg"), use "upload file" on the left
}}</pre>
The order of the fields is not relevant. The template should be given as the ''first'' thing on a page.

</noinclude><includeonly>
{| style="padding:10px" align="right" cellpadding="2" cellspacing="0" width="350px"

|-
{{Tablelongrow|Color=#336633|Style=border-top: 1px solid #808080;|Value={{{firstname}}} {{{lastname}}}}}

|-
{{#ifeq:{{{photo|}}}|||{{Tablelongrow|Value='''[[Image:{{{photo}}}|200px|Foto di {{PAGENAME}}]]'''}}}}

|-
{{#ifeq:{{{firstname|}}}|||{{Tablerow|Label=First Name|Value={{{firstname}}}}}}}

|-
{{#ifeq:{{{lastname|}}}|||{{Tablerow|Label=Last Name|Value={{{lastname}}}}}}}

|-
{{#ifeq:{{{email|}}}|||{{Tablerow|Label=E-Mail|Value={{Mailbox|{{{email}}}}}}}}}

|-
{{#ifeq:{{{advisor|}}}|||{{#arraymap:{{{advisor|}}}|;|x|[[hasAdvisor::User:x| ]]|}}}}
{{#ifeq:{{{advisor|}}}||| {{Tablerow|Label=Advisor|Value=<ul>{{#arraymap:{{#ask: [[User:{{PAGENAME}}]] | ?hasAdvisor= }}|, |x|<li>x</li>|}}</ul>}} }}

|-
{{#ifeq:{{{resarea|}}}|||{{#arraymap:{{{resarea|}}}|;|x|[[hasResArea::x| ]]|}}}}

{{Tablerow|Label=Research Areas|Value={{#ask: [[User:{{PAGENAME}}]] | ?hasResArea= }}}}
|}[[Category:Researcher]]</includeonly>

Template:Researcher

2010-09-29T16:45:40Z

RiccardoTasso:

Wikipedia Category Graph

2010-09-21T13:27:32Z

RiccardoTasso:

{{Project
|title=Wikipedia Category Graph
|short_descr=Represent Wikipedia Categories with a model based on graphs to further analyze it.
|coordinator=MarcoColombetti
|tutor=DavidLaniado;RiccardoTasso
|students=JacopoFarina;
|resarea=Social Software and Semantic Web
|restopic=Graph Mining and Analysis
|start=2010/06/10
|end=2010/10/01
|status=Closed
|level=Bs
|type=Thesis
}}
The goal of the project is to analyze Wikipedia categories by representing them in a graph based database.

Wikipedia categories are not a three-based structure: a category may be contained in another one which is contained in another one which is contained in the first, generating a cyclic reference and many categories may be a root category (non contained in others).

For this reasons a graph database is better to represent the structure.
==Creation and further analysis of the database with igraph==
Wikipedia let users download the entire site database (with all versions of all articles) or just some selections of it.
We use a selection which contains the category list and articles memberships in them.

[http://neo4j.org/ Neo4j] is a graph-based database, which allow a program to create and manipulate graph structures like nodes and relationships.

In order to transfer the database in neo4j format is better save it in a file, which will be read one line at time.

After transferring the structure in a Neo4j graph is possible to create from it a Pajek file (.net) to make general analysis like described [[Social_Network_Analysis_With_Igraph_Package_Using_R|here]].

==Results of the analysis==
We can use the tools described [http://igraph.sourceforge.net/doc/R/00Index.html here]

The '''diameter''' of the graph is '''32'''. This is the maximum distance (number of nodes in the minimum path) between two nodes. These two nodes are ''Prehistoric life sorted by geography'' (A category about prehistoric animals without articles) and ''BMW M20'' (a car).

The '''average distance''' between two nodes is '''5.5568'''.

The '''graph density''' is '''6.28*10-7'''. This is the ratio of the number of edges and the number of possible edges.

By analysing the graph with little Java programs written for this task we calculated the average number of categories per article is '''2.68'''.

Statistically, the 93% of articles has less than 7 categories, 64% less than 3.

The article with more categories is [http://en.wikipedia.org/wiki/Winston_Churchill Winston Churchill], with 70 categories. [http://en.wikipedia.org/wiki/Albert_einstein Albert Einstein] has the notable number of 56 categories, too.

===Strongly connected components===

By applying the Tarjan's strongly connected components algorithm to the graph is possible to found 93 structures of up to 2 nodes. Each of them contains at least one cycle. Most of them are composed of two categories about the same thing, like ''History of the Germanic peoples'' and ''Ancient Germanic peoples'', but there are also more curious cases like this one
[[Image:Struttura fortemente connessa wikipedia.png|400px]]

=== Tested algorithms ===
We tried 9 algorithms to choose the category which fit best an article. After confronting the results of the automatic procedure with human made assignments, the best algorithm was choose a different weight to each edge by the traversal direction.
Sizes of the macrocategories determined this way are:
[[Image:Dimensioni macrocategorie costi.png|400px]]

==Download==
*The relation about the project (in Italian) can be found [[Media:Relazione progetto WikipediaCatGraph.pdf|here]]
*Sources in Java can be found [[Media:Wikipedia category Graph-sources.zip|here]]
*Slides of the thesis (in Italian) [[Media:Presentazione wikipedia category graph.pdf|here]]
*The tesis (in Italian) [[Media:Tesi wikipedia category graph.pdf|here]]

==Previous Work==

*[http://www-users.cs.umn.edu/~echi/papers/2009-CHI2009/p1509.pdf What's in Wikipedia? Mapping Topics and Conflict Using Socially]

User:JacopoFarina

2010-09-21T13:27:09Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Jacopo
|lastname=Farina
|email=jacopo1.farina@mail.polimi.it
|advisor=RiccardoTasso; DavidLaniado;
|status=inactive
}}
I'm currently working on a third-year thesis about [[WikipediaCategoryGraph|analyzing the Wikipedia graph of categories and pages]].

User:MicheleMonti

2010-07-21T20:08:14Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Michele
|lastname=Monti
|photo=MicheleMonti.jpg
|email=michimonti@tiscali.it
|projectpage=Friendship Recommendation System based on a Social Network Topological Analysis
|advisor=DavidLaniado;RiccardoTasso
|status=inactive
}}
Nato a Lecco il 09/10/1984.

'''Attualmente''' tesista presso AIRLab sede del DEI.

User:LeonardoBruni

2010-07-21T20:08:01Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Leonardo
|lastname=Bruni
|photo=LeonardoBruni.jpg
|email=leonardo1.bruni@mail.polimi.it
|advisor=DavideEynard;DavidLaniado;RiccardoTasso
|status=inactive
}}
== Projects ==
<ul>

<li>
;[[Social Network Analysis]]:
{{#ask: [[Category:Project]][[prjResTopic::Social Network Analysis]][[prjStudent::User:{{PAGENAME}}]]|?prjTitle = |format=ul}}
</li>

<li>
;[[Semantic Search]]:
{{#ask: [[Category:Project]][[prjResTopic::Semantic Search]][[prjStudent::User:{{PAGENAME}}]]|?prjTitle = |format=ul}}
</li>

</ul>

User:FabioMarfia

2010-07-21T20:07:44Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Fabio
|lastname=Marfia
|photo=FabioMarfia.jpg
|email=fabio@chords.it
|projectpage=Extraction
|advisor=DavideEynard; MatteoMatteucci;
|status=inactive
}}
Fabio Marfia,
matricola 718843

User:FedericoVilla

2010-07-21T20:05:13Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Federico
|lastname=Villa
|photo=Federico_villa.jpg
|email=federico.villa@gmail.com
|projectpage=Strategic Multi Robot Patrolling
|advisor=NicolaGatti
|status=inactive
}}

Jobe

2010-07-20T08:00:33Z

RiccardoTasso: /* Jobe */

= Who is jobe =
[[Image:PE1900_Spec_Sheet_Quad.pdf]]

File:PE1900 Spec Sheet Quad.pdf

2010-07-20T08:00:00Z

RiccardoTasso: Jobe specification sheet

Jobe specification sheet

Jobe

2010-07-20T07:59:25Z

RiccardoTasso: New page: = Jobe = Image:PE1900_Spec_Sheet_Quad.pdf

= Jobe =
[[Image:PE1900_Spec_Sheet_Quad.pdf]]

User:VanVungPham

2010-06-24T14:48:46Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Van Vung
|lastname=Pham
|email=phamvanvung@yahoo.com
|projectpage=Firefox Extension for Semantic Annotation
|advisor=DavideEynard; DavidLaniado; RiccardoTasso;
|status=inactive
}}

Firefox Extension for Semantic Annotation

2010-06-24T14:48:30Z

RiccardoTasso:

{{Project
|title=Firefox Extension for Semantic Annotation
|proposal=A firefox extension for semantic annotations
|coordinator=DavideEynard
|tutor=DavideEynard; DavidLaniado; RiccardoTasso;
|students=VanVungPham;
|resarea=Social Software and Semantic Web
|restopic=Semantic Annotations
|start=2009/10/30
|end=2010/02/15
|status=Closed
|level=Ms
|type=Course
|report=[[Image:AIRLabReport.pdf‎]]
}}
[[Category:Project]]
==Report==
===Introduction===
In every area of software development, the ease in extensibility and efficiency of the development is always a practical issue, especially in the world of JavaScript which is difficult to debug when things go wrong. Furthermore, it is always a benefit of having a generic methodology which allows the developers to develop applications in a declarative way using RDF technologies.

In this AIRLab project I would like to present a methodology which tries to satisfy the mentioned requirements. This methodology will present the development steps, design architectures, and technologies in development of extensible Firefox extension for Semantic Annotations.

The report first analyzes the generic components of Firefox extensions for semantic annotations to have clear idea of steps that should be followed to develop an extension providing semantic annotation functionalities. Basing on this analysis, the code for the generic components is implemented. Then later the programmers can just declaratively describe the components, using RDF technologies, and the code will automatically generate them based on the developers’ descriptions.

The report will next describes about each of the components, how they are described in RDF and how to use them in JavaScript code if needed.

For the developers who would like to extend this framework, there is also a part describing the coding ideas of this development methodology and the possible extensions of this project.
===Development Architecture===
Development architecture of this Firefox Extension for Semantic Annotations is depicted as follows:

[[Image:DesignArchitecture.JPG‎]]

For detail explanation of the architecture please refer to the PDF version of the report in the Downloads section.
==Downloads==
===PDF of the Report===
* [[Media:AIRLabReport.pdf]]
===The developed extensions===
* [[Media:Salinux.zip]] (Linux version)
* [[Media:Sawindow.zip]] (Windows version)
===Sample Joseki configuration file===
* [[Media:Joseki-config.zip]]
==Deployment==
===Installation===
The installation platform was tested in Windows Vista, Windows XP and Ubuntu 9.10. There are two versions of this, one for Windows and another for Linux. The Linux version is only different from that for Windows by changing the “\\” to “/” in some parts of the code for file/directory paths. There is a readme.txt file inside each of the folder.

1. Install MySQL (http://www.mysql.com)

2. Create a database named "annotationdb", or any other name as long as you will change it in the [[Media:Joseki-config.zip]] file to reflect this.

3. Install Joseki (tested version is 3.4.1) - (http://joseki.sourceforge.net/)

4. Add the data store (the database created) and setup the sparql read endpoint (read action) and the sparql update endpoint (update action) (please refer to [[Media:Joseki-config.zip]] file to see an example about setting up the enpoints to use MySQL DB as RDF Store).

5. In the sample file ([[Media:Joseki-config.zip]]) the lines starting from line 172 to the end are the setup used to set the RDF data store to "annotatiodb" database in MySQL Server (localhost). The read action is "sparql/myproject/read", and the update action is "sparql/myproject/update"

6. Install the *.xpi, the [[Media:Sawindow.zip]] is for Window version and [[Media:Salinux.zip]] is for Linux version.

7. If you changed your joseki host (currently http://localhost:2020/), or your read action (currently "sparql/myproject/read"), or your update action (currently "sparql/myproject/update"), or the default user name (currently annonymous@annonymous.com). You will need to change this default options after installing the add-on. The add-on dialog (In Firefox: Toos > Add-ons). In our add-on there is an Options Button(for Window) Preferences Button (for Linux), you can click on that and change your host, read action, update action, and user, correspondingly.

===Note===
We kept the application reasonably simple, since the idea is to demonstrate functional flexibility of this extension for semantic annotation. The extension is a reasonably flexible and extensible Firefox extension. However, the part that would be kind of trouble is it takes little time to initialize the extension when Firefox is loaded, due to the “eager” loading manner (not “lazy” loading). At the first time the menu is loaded into Firefox browser, the extension starts to read the RDF setting files to create all the objects for menus, menu items, menu popup, queries, functions, etc and these would take time. Therefore, user may experience a little slow performance at the first time user loads the menu (when user hovers the Semantic Annotation menu).

The good thing is that this loading will appear only once and the program will be executed faster since all the objects are loaded and ready. The “lazy” loading method may let user see the program is loaded quickly. However, it would be slow at runtime due to the need of loading objects at run-time.
==Development resources==
===JavaScript Function (Object)===
The main development idea started from the possibility to create new Function (Object in JavaScript) from its string descriptions.

"Function - MDC"
* https://developer.mozilla.org/en/Core_JavaScript_1.5_Reference/Global_Objects/Function
===Loading local files with Firefox===
In this extension we need to load the local files from local disk of computer which is running Firefox browser.

This uses Firefox nsILocalFile interface.
* https://developer.mozilla.org/en/nsILocalFile

===JavaScript RDF Parser===
This is an RDF Parser which we use to parse RDF setting file.
"Hercules RDF Library in JavaScript: SPARQL Engine Demo"
* http://www.arielworks.net/works/codeyard/hercules/demo/index.html

===RDF Server===
Joseki - A SPARQL Server for Jena
* http://joseki.sourceforge.net/
===Ontology===
Only the Tagging Ontology and its design decisions are used in this development. The other two ontology are put here as references.

"Tagging Ontology"
* http://www.holygoat.co.uk/projects/tags/
"Ratings Ontology"
* http://www.tvblob.com/index.php?option=com_content&task=view&id=128
"Sioc Ontology”
* http://sioc-project.org/ontology

===SPARQL===
"SPARQL Protocol for RDF",
* W3C Recommendation 15 January 2008, http://www.w3.org/TR/rdf-sparql-protocol
"SPARQL Query Language for RDF"
* W3C Recommendation 15 January 2008, http://www.w3.org/TR/rdf-sparql-query

Social Network Analysis With Igraph Package Using R

2010-06-20T15:00:50Z

RiccardoTasso:

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
'''graph size''' (number of nodes): length(V(g))

'''number of edges''': length(E(g))

'''diameter''': diameter(g)

'''mean degree''':

'''density''':

'''giant component size''' (the largest connected component size):

assortativity:

== Node Centrality Analysis ==

'''degree''': degree(g)

'''closeness''': closeness(g)

'''betweenness''': betweenness(g)

'''eigenvector'''/'''pagerank''': evcent(g)

== Clustering Techniques ==

'''connected components''': clusters(g)

'''edge betweenness''':

'''walktrap''':

'''spinglass''':

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Wikipedia Category Graph

2010-06-16T15:37:39Z

RiccardoTasso:

Wikipedia Category Graph

2010-06-10T14:34:59Z

RiccardoTasso:

Wikipedia Category Graph

2010-06-10T14:34:40Z

RiccardoTasso:

Wikipedia Category Graph

2010-06-10T14:31:56Z

RiccardoTasso: New page: {{Project | title=Wikipedia Category Graph | short_descr= Tool for managing categories and pages in Wikipedia. | tutor=DavidLaniado;RiccardoTasso | students=CarloBandera | resarea=Soc...

{{Project
| title=Wikipedia Category Graph
| short_descr= Tool for managing categories and pages in Wikipedia.
| tutor=DavidLaniado;RiccardoTasso
| students=CarloBandera
| resarea=Social Software and Semantic Web
| restopic=Semantic Tagging
| start=2009/07/14
| end=2009/09/17
| level=Ms
| type=Course
| status=Closed
| image=
}}

User:JacopoFarina

2010-06-10T14:29:37Z

RiccardoTasso:

{{Student
|category=Student
|firstname=Jacopo
|lastname=Farina
|email=jacopo1.farina@mail.polimi.it
|advisor=RiccardoTasso; DavidLaniado;
|status=active
}}

User:JacopoFarina

2010-06-10T14:29:21Z

RiccardoTasso: New page: {{Student |category=Student |firstname=Jacopo |lastname=Farina |email=jacopo1.farina@mail.polimi.it |advisor=RiccardoTasso; DavidLaniado; |status=active }}

{{Student
|category=Student
|firstname=Jacopo
|lastname=Farina
|email=jacopo1.farina@mail.polimi.it
|advisor=RiccardoTasso; DavidLaniado;
|status=active
}}

Batch Learning for Poker

2010-05-21T18:51:25Z

RiccardoTasso:

{{Project
|title=MRT: Batch Learning for Poker
|coordinator=MarcelloRestelli
|students=RafaelVilella
|resarea=Machine Learning
|start=2010/03/01
|end=1970/01/01
|status=Active
|type=Thesis
}}
=== Project short description ===

This project is aimed at determination an efficient policy to play Poker Limit Hold`em Heads up. Subdivided in three major areas:
1- Analysis of rules of poker and player strategies.
2- Feature Extraction: Given the fact that number of possible states are quite large (10E14). A FS approach is necessary.
3- Fitted Q-Iteration to determinate the policy using Extremely Randomized Trees.

=== Dates ===
Start date: 2010/03/01

End date: Still in progress

===== Project head(s) =====

[[User:MarcelloRestelli|M.Restelli]]

===== Students currently working on the project =====

[[User:RafaelVilella|Rafael Domingues Santos Vilella]]

=== Laboratory work and risk analysis ===

This project is related to software developing so there are no dangerous activities

Batch Learning for Poker

2010-05-21T18:51:00Z

RiccardoTasso:

{{Project
|title=MRT: Batch Learning for Poker
|coordinator=MarcelloRestelli
|students=RafaelVilella
|resarea=Machine Learning
|start=2010/03/01
|end=1970/01/01
|status=Active
|type=Thesis
}}
=== Project short description ===

This project is aimed at determination an efficient policy to play Poker Limit Hold`em Heads up. Subdivided in three major areas:
1- Analysis of rules of poker and player strategies.
2- Feature Extraction: Given the fact that number of possible states are quite large (10E14). A FS approach is necessary.
3- Fitted Q-Iteration to determinate the policy using Extremely Randomized Trees.

=== Dates ===
Start date: 2010/03/01

End date: Still in progress

===== Project head(s) =====

- [[User:MarcelloRestelli|M.Restelli]]

===== Students currently working on the project =====

Rafael Domingues Santos Vilella - [[User:RafaelVilella]]

=== Laboratory work and risk analysis ===

This project is related to software developing so there are no dangerous activities

Social Network Analysis With Igraph Package Using R

2010-04-20T08:50:00Z

RiccardoTasso:

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

assortativity:

== Node Centrality Analysis ==

degree: degree(g)

closeness: closeness(g)

betweenness: betweenness(g)

eigenvector/pagerank: evcent(g)

== Clustering Techniques ==

connected components: clusters(g)

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:44:27Z

RiccardoTasso: /* Macroscopic Analysis */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

assortativity:

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:44:20Z

RiccardoTasso: /* Macroscopic Analysis */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

assortativity:

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:44:08Z

RiccardoTasso: /* Node Centrality Analysis */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:39:22Z

RiccardoTasso: /* Starting with the package */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
tkplot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:36:20Z

RiccardoTasso:

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes): length(V(g))

number of edges: length(E(g))

diameter: diameter(g)

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-20T08:33:54Z

RiccardoTasso:

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes):

number of edges: length(V(g))

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis

2010-04-03T11:36:31Z

RiccardoTasso:

[[Category:Research Topic]]
This research topic belongs to the research area [[belongsToArea::Social Software and Semantic Web]].

== Projects ==

Projects on this topic:

{{#ask: [[Category:Project]][[prjResTopic::{{PAGENAME}}]]|?prjTitle = |format=ul}}

== Project proposals ==

{{#ask: [[Category:ProjectProposal]]
[[PrjResTopic::{{PAGENAME}}]]|
?PrjTitle |
?PrjImage |
?PrjDescription |
?PrjTutor |
?PrjStarts |
?PrjStudMin |
?PrjStudMax |
?PrjCFUMin |
?PrjCFUMax |
?PrjResArea |
?PrjResTopic |
?PrjLevel |
?PrjType |
format = template |
template = Template:ProjectProposalViz
}}

== Further Readings ==

==== What is Social Network Analysis? ====

* [http://vimeo.com/2477265 How Kevin Bacon Cured Cancer - Part 1]
* [http://vimeo.com/2477361 How Kevin Bacon Cured Cancer - Part 2]
* [http://vimeo.com/2477459 How Kevin Bacon Cured Cancer - Part 3]

==== Tools for Social Network Analysis (and visualization) ====

* http://jung.sourceforge.net
* http://igraph.sourceforge.net/
* http://nwb.slis.indiana.edu/

==== Tools for data visualization ====

* http://processing.org/
* http://www.piccolo2d.org/
* http://graphexploration.cond.org/
* http://www.tulip-software.org/
* http://gephi.org/

==== Ontologies and vocabularies for social network data representation in the Semantic Web ====

* http://www.foaf-project.org/
* http://sioc-project.org/

==== Tutorials ====
* [[Social Network Analysis With Igraph Package Using R]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:33:27Z

RiccardoTasso: /* Install the package under R environmemnt */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command to download and install igraph as R library:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes):

number of edges:

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:32:53Z

RiccardoTasso: /* Install the package under R environmemnt */

== Install the package under R environmemnt ==

under Ubuntu install the R package:
<code>sudo apt-get install r-base-core</code>

type R to start the program:
<code>R</code>

type the command:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes):

number of edges:

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:29:13Z

RiccardoTasso: /* Macroscopic Analysis */

== Install the package under R environmemnt ==

type the command:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes):

number of edges:

mean degree:

density:

giant component size (the largest connected component size):

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:27:38Z

RiccardoTasso:

== Install the package under R environmemnt ==

type the command:
<code>install.packages("igraph")</code>

== Starting with the package ==
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>

== Macroscopic Analysis ==
graph size (number of nodes):

number of edges:

mean degree:

== Node Centrality Analysis ==

degree:

closeness:

betweenness:

eigenvector/pagerank:

assortativity:

== Clustering Techniques ==

connected components:

edge betweenness:

walktrap:

spinglass:

== References and further informations ==

Igraph/R documentation: http://igraph.sourceforge.net/doc/R/00Index.html

[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:21:35Z

RiccardoTasso:

= Install the package under R environmemnt =

type the command:
<code>install.packages("igraph")</code>

= Starting with the package =
don't forget to load the library:
<code>library('igraph')</code>
parse a network file in Pajek format and access to its memory representation through the variable 'g':
<code>g <- read.graph(file='/your/path/.../file.net', format='pajek')</code>
list vertex and their attributes of graph g:
<code>
V(g) # the list of all nodes
V(g)[0] # access the first node
list.vertex.attributes(g) # the list of all vertex attributes names
V(g)[0]$id # access the attribute 'id' for the first node
</code>
list edges/arcs and their attributes of graph g:
<code>
E(g) # the list of all edges/arcs
E(g)[0] # access the first edge/arc
list.edge.attributes(g) # the list of all edges/arcs attributes names
E(g)[0]$weight # access the attribute 'weight' for the first node
</code>
draw the graph:
<code>
plot(g)
</code>
[[Category:Tutorial]]

Social Network Analysis With Igraph Package Using R

2010-04-03T11:20:20Z

RiccardoTasso: /* Starting with the package */

Social Network Analysis With Igraph Package Using R

2010-04-03T10:30:54Z

RiccardoTasso:

Social Network Analysis With Igraph Package Using R

2010-04-03T10:25:39Z

RiccardoTasso:

Social Network Analysis With Igraph Package Using R

2010-04-03T10:21:15Z

RiccardoTasso: /* Starting with the package */

Social Network Analysis With Igraph Package Using R

2010-04-03T10:16:21Z

RiccardoTasso: New page: = Install the package under R environmemnt = type the command: <code>install.packages("igraph")</code> = Starting with the package = don't forget to load the library: <code>library('i...

Category:Tutorial

2010-04-03T10:07:41Z

RiccardoTasso: New page: This category will include Tutorials of all possible kinds and possibly it should be "semantified" linking a tutorial with a list of research topics.

This category will include Tutorials of all possible kinds and possibly it should be "semantified" linking a tutorial with a list of research topics.

Friendship Recommendation System based on a Social Network Topological Analysis

2010-04-02T16:26:21Z

RiccardoTasso:

{{Project
|title=Facebook Network Analysis
|coordinator=MarcoColombetti
|tutor=DavidLaniado;RiccardoTasso
|students=MicheleMonti
|resarea=Social Software and Semantic Web
|restopic=Social Network Analysis
|start=2009/07/27
|end=2010/05/03
|status=Active
|level=Ms
|type=Thesis
}}

Friendship Recommendation System based on a Social Network Topological Analysis

2010-04-02T16:25:30Z

RiccardoTasso:

{{Project
|title=Facebook Network Analysis
|coordinator=MarcoColombetti
|tutor=DavidLaniado;RiccardoTasso
|students=MicheleMonti
|resarea=Social Software and Semantic Web
|restopic=Social Network Analysis;Bio Robotics
|start=2009/07/27
|end=2010/05/03
|status=Active
|level=Ms
|type=Thesis
}}

Property:PrjResArea

2010-04-02T16:17:32Z

RiccardoTasso:

This property is used to indicate that a [[:Category:Project|Project]] belongs to one (ore more) [[:Category:Research Area|Research Area]].

Allowed values are all [[:Category:Research Area|research areas]]: {{#arraymap: {{#ask: [[Category:Research Area]]|format=list|link=none|sep=;}}|;|x|[[allows value::x]]}}