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![Centrality: strategic positions
degree centrality focuses on
the number of relations:
reveal local popularity
[Freeman 1979]](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-15-320.jpg)
![Centrality: strategic positions
closeness centrality focuses on
the proximity of actors:
reveal capacity to communicate
[Freeman 1979]](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-16-320.jpg)
![Centrality: strategic positions
beetweenness centrality focuses
on intermediary actors
reveals brokers and privileged
actor in the information flow
[Freeman 1979]
"A place for good ideas"
[Burt 1992] [Burt 2004]](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-17-320.jpg)
![tendency to bind with similar others
"Birds of a feather flock together"
"interaction produces similarity, while
similarity produces interaction"
[Mika 2005]](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-21-320.jpg)
![Balance Theory
[Heider 1958]](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-24-320.jpg)
![You can now handle your
social capital
The social capital is the "resources embedded in one's
social networks, resources that can be accessed or
mobilized through ties in the networks" [Lin 2008]
http://www.kstoolkit.org/Social+Network+Analysis](https://image.slidesharecdn.com/socialnetworkanalysis-101116105830-phpapp02/85/Social-network-analysis-course-2010-2011-49-320.jpg)
Uploaded byguillaume ereteo
Social network analysis course 2010 - 2011
This document discusses social network analysis and its applications. It defines a social network as being composed of actors (people or groups) connected by social relationships. Social network analysis can be used to map these relationships visually using sociograms, understand information flow and community structure, and identify influential actors through metrics like centrality and betweenness. Tools like NodeXL and Gephi enable network extraction, visualization, and analysis to glean strategic insights from social networks.
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Social network analysis course 2010 - 2011
- 1.
- 2. Social Networks A socialnetwork is made of actors (people, organizations, group) that are tied by social links.
- 3. Sociograms and graphs actorsare represented by points (nodes, vertices) and relations by lines (edges, links, arcs)
- 4. • explicit anddeclared relations • interactions between actors • affiliation between actors Social links colleague communicate like like web
- 5. undirected networks well suitedfor representing network with symmetric relationships such as facebook
- 6. directed networks well suitedfor representing network with non reciprocal relationships such as Twitter
- 7. weighted networks well suitedfor representing the intensity of relationships, the number of interactions (e.g. mails), or the number of affiliations (e.g. shared links). 1 0.5 1 2 3
- 8. labelled networks well suitedfor representing the type of relationships family colleague father friend follow follow father
- 9. Social network analysis helpsunderstanding and exploiting the key features of social networks in order to manage their assets, their life cycle and predict their evolution.
- 10. What for? • Tocontrol information flow • To foster communication • To improve network resilience • To trust or not
- 11. Diameter the maximum distancebetween two actors
- 12. Density of links indicatesthe cohesion of the network
- 13.
- 14. community membership • influencesthe way information is shared • influences the way actors behave
- 15. Centrality: strategic positions degreecentrality focuses on the number of relations: reveal local popularity [Freeman 1979]
- 16. Centrality: strategic positions closenesscentrality focuses on the proximity of actors: reveal capacity to communicate [Freeman 1979]
- 17. Centrality: strategic positions beetweennesscentrality focuses on intermediary actors reveals brokers and privileged actor in the information flow [Freeman 1979] "A place for good ideas" [Burt 1992] [Burt 2004]
- 18. Centrality: strategic positions eigenvector centrality focuses on the connections of neighbours reveals actors that are well connected
- 20. transitivity of relationships evaluatingsocial opportunities Peter Jack Paul
- 21. tendency to bindwith similar others "Birds of a feather flock together" "interaction produces similarity, while similarity produces interaction" [Mika 2005]
- 22.
- 23. who will increaseits popularity?
- 24.
- 25.
- 26. Many ways of connectingpeople! likes ingredient typemainDish Food subclassOf type
- 27. Who has thebest betweeness? this is a matter of semantic knows in passing has met colleague works With works With friend
- 28. Metrics are notsufficient enough knows in passing has met colleague works With works With friend
- 29. examples of vizualisationand analysis tools
- 30. Touchgraph facebook browser easilyvizualize your facebook social network http://apps.facebook.com/touchgraph
- 31.
- 32. Analyze your facebooknetwork 1. Extract it first with netvizz: http:// apps.facebook.com/netvizz 2. Open it with my favorite graph visualization tool: Gephi http:// www.gephi.org
- 33. extract with Netvizz •choose the information to include • Save the file
- 34.
- 35. Your network isundirected
- 36. select the forcelayout
- 37.
- 38.
- 39. compute shortest pathsof relations
- 40. highlight most popularactors
- 41.
- 42.
- 43. NODEXL • Excel 2007based social network visualization and analysis tool • Homepage http://nodexl.codeplex.com • Tutorial http://casci.umd.edu/images/4/46/NodeXL_tuto
- 44. Quick overview ofthe tutorial
- 45. "One way tobegin using NodeXL is to type in your own edge list. For example, you might type the name of people who are friends in each row filling in the Vertex 1 and Vertex 2 columns"
- 46. "Click on theShow Graph button (directly above the graph pane) to show the network of friendships"
- 47. "To calculate graphmetrics first click on the Graph Metric button on the Analysis section of the NodeXL Ribbon."
- 48. "Vertex metrics canbe mapped onto visual attributes. The graph legend shows that Degree is mapped to Size and Betweenness Centrality is mapped to Opacity."
- 49. You can nowhandle your social capital The social capital is the "resources embedded in one's social networks, resources that can be accessed or mobilized through ties in the networks" [Lin 2008] http://www.kstoolkit.org/Social+Network+Analysis
- 50.
Editor's Notes
- #12 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #13 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #14 Community detection helps understanding the global structure of a network and the distribution of actors and activities. Moreover, the community structure influences the way information is shared and the way actors behave. Information spread quickly in a community and is shared by most of it members.
- #15 Community detection helps understanding the global structure of a network and the distribution of actors and activities. Moreover, the community structure influences the way information is shared and the way actors behave. Information spread quickly in a community and is shared by most of it members.
- #16 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #17 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #18 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #19 The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman. The degree centrality considers nodes with the higher degrees (number of adjacent edges). The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached. The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
- #25 La théorie de l'équilibre de Heider considère que le produit des sentiments dans un réseaux social doit être positif pour que les interactions dans le réseau soient dans un état d'équilibre.
- #26 "Il semble que le terme « masse critique » vienne d'une observation du trafic routier en Chine, où sans feux de signalisation aux croisements, les cyclistes attendent d'être assez nombreux, de faire masse pour s'engager et traverser ensemble. " Le principe de masse critique dans un réseau social correspond à un niveau d'activité à partir duquel le réseau change d'état de manière permanente. Par exemple, à partir d'un certain nombre de personne et d'un volume d'activité minimale d'interaction, un groupe de personne devient une communauté d'intérêt.
- #27 Semantic Web benefit: Platform interoperability, common models Structured Actors, structured resources Different semantic link different community detection perspectives cross communities membership!!! Semantic of interaction semantic community detection!!!
- #32 Community detection helps understanding the global structure of a network and the distribution of actors and activities. Moreover, the community structure influences the way information is shared and the way actors behave. Information spread quickly in a community and is shared by most of it members.