Social network analysis course 2010 - 2011
•Descargar como PPT, PDF•
29 recomendaciones•8,792 vistas
the 5th social media course at skema business school about social network analysis
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Destacado
Destacado (20)
Similar a Social network analysis course 2010 - 2011
Similar a Social network analysis course 2010 - 2011 (20)
Más de guillaume ereteo
Más de guillaume ereteo (13)
Último
Último (20)
Social network analysis course 2010 - 2011
- 1. Social Network Analysis Guillaume ERETEO
- 2. Social Networks A social network is made of actors (people, organizations, group) that are tied by social links.
- 3. Sociograms and graphs actors are represented by points (nodes, vertices) and relations by lines (edges, links, arcs)
- 4. • explicit and declared relations • interactions between actors • affiliation between actors Social links colleague communicate like like web
- 5. undirected networks well suited for representing network with symmetric relationships such as facebook
- 6. directed networks well suited for representing network with non reciprocal relationships such as Twitter
- 7. weighted networks well suited for 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 suited for representing the type of relationships family colleague father friend follow follow father
- 9. Social network analysis helps understanding and exploiting the key features of social networks in order to manage their assets, their life cycle and predict their evolution.
- 10. What for? • To control information flow • To foster communication • To improve network resilience • To trust or not
- 11. Diameter the maximum distance between two actors
- 12. Density of links indicates the cohesion of the network
- 13. community detection distribution of actors and activities
- 14. community membership • influences the way information is shared • influences the way actors behave
- 15. Centrality: strategic positions degree centrality focuses on the number of relations: reveal local popularity [Freeman 1979]
- 16. Centrality: strategic positions closeness centrality focuses on the proximity of actors: reveal capacity to communicate [Freeman 1979]
- 17. 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]
- 18. Centrality: strategic positions eigen vector centrality focuses on the connections of neighbours reveals actors that are well connected
- 20. transitivity of relationships evaluating social opportunities Peter Jack Paul
- 21. tendency to bind with similar others "Birds of a feather flock together" "interaction produces similarity, while similarity produces interaction" [Mika 2005]
- 23. who will increase its popularity?
- 25. Critical mass
- 26. Many ways of connecting people! likes ingredient typemainDish Food subclassOf type
- 27. Who has the best betweeness? this is a matter of semantic knows in passing has met colleague works With works With friend
- 28. Metrics are not sufficient enough knows in passing has met colleague works With works With friend
- 29. examples of vizualisation and analysis tools
- 30. Touchgraph facebook browser easily vizualize your facebook social network http://apps.facebook.com/touchgraph
- 31. this is mine
- 32. Analyze your facebook network 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
- 35. Your network is undirected
- 36. select the force layout
- 39. compute shortest paths of relations
- 40. highlight most popular actors
- 42. print attributes
- 43. NODEXL • Excel 2007 based social network visualization and analysis tool • Homepage http://nodexl.codeplex.com • Tutorial http://casci.umd.edu/images/4/46/NodeXL_tuto
- 44. Quick overview of the tutorial
- 45. "One way to begin 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 the Show Graph button (directly above the graph pane) to show the network of friendships"
- 47. "To calculate graph metrics first click on the Graph Metric button on the Analysis section of the NodeXL Ribbon."
- 48. "Vertex metrics can be mapped onto visual attributes. The graph legend shows that Degree is mapped to Size and Betweenness Centrality is mapped to Opacity."
- 49. 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
Notas del editor
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- "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.
- 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!!!
- 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.