Social Network Analysis for small learning groups
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This document provides an overview of social network analysis (SNA). It defines key SNA concepts like nodes, edges, networks and visualization of networks using graphs. It discusses different types of networks and models for network growth. It also covers centrality measures to determine node importance, methods for identifying communities, and software tools like Netlogo and Gephi for SNA. Examples of visualizing online student interaction networks in Moodle and Google+ using these concepts and tools are also presented.
Social Network Analysis for small learning groups
- 1. Social Network Analysis Jutta Pauschenwein ZML-Innovative Lernszenarien FH JOANNEUM
- 2. 2
- 3. COS16 – week 1 3
- 4. 4 Coursera MOOC: 8 weeks, autumn 2014
- 5. Why SNA? I want to understand data through visualization Woche 1 im SNA-MOOC 5
- 6. Definitions • network: set of connected nodes (social: connection via relation-ship) • nodes: nodes, actors, sites, vertices • connections: edges, ties, relations • visualize networks by graphs 4 communities MOOC, week1 6
- 7. Questions structure of the network • Are the nodes connected? How far are they from each other? Are some nodes more important than others? Are there communities in the network? types if networks • randomly generated connections, network with preferences, small world networks (most nodes are not neighbours of one another, but the neighbours of any give node are likely to be neighbours of each other) small-world-network, MOOC week 5 7
- 8. • connections are directed / not directed • weighted connection • a node with several degrees A communciates with B A communcates with B and B with A A communciated with B 4x Connections 8 Communication of students in google+ 4 days
- 9. Erdős-Rényi Graph • simple network with fixed number of nodes • assumption 1: nodes connect randomly • assumption 2: network is not directed • assumption 3: N nodes, M connections, p probability that two nodes connect • in this network type there appear no hubs, but the „giant component“ 9
- 10. Reale networks grow Expansion of the Erdős-Rényi approach • growing networks, for example WWW, citation networks Models • random preferential: new nodes prefer to connect to already well connected nodes • introduction model: nodes were presented to each other • static geographic model: nodes connect to the neighbours of the nodes they are connected with 10
- 11. Barabasi-Albert model • there’s a probabilty that each node connects with another node in dependence of its degree • There’s an initial configuration and then the process of connecting starts with an • each new node has a certain probability of m to connect to the network 11
- 13. in the preferential model you get hubs because „old“ nodes have more time to connect 13
- 14. Centrality What importance has a node in the network? 14
- 15. 15 degree centrality the node is an active player in the network, it is well connected
- 16. 16 betweeness centrality „broker“ - all communication uses this node If the node doesn’t work, the connection in the network fails.
- 17. 17 closeness Closeness is the distance of one node to all the other nodes - it is enough to be near a hub
- 18. 18 eigenvector centrality The importance of a node increases with the importance of its neighbours.
- 19. 19 Which node has a small degree but a high betweeness? Or reversed?
- 20. Find communities How to define a community / substructur in a network? • There are many connections within a community. • The other nodes in a community are only at a distance of some hops. • Nodes in one community are strongly connected. It’s difficult to find communities if you don’t know the number of communities - there are large and small communities. Parameters: • minimum cut: number of communities • hierarchical clustering: clustering based on certain characteristics • betweennes clustering: connections with the highest betweennes are removed 20
- 21. 21 Problem: when do you stop to remove connections? => Modularity - comparison, how many connections are within and outside of the community In a network the number of connections within a community increases and the number of connections to nodes outside of the community decreases http://spark-public.s3.amazonaws.com/sna/other/guess/betweennessclust.html
- 22. 3 communities of my facebook friends (2014)
- 23. Software 23 Netlogo • programmable modeling environment for simulating natural and social • phenomena • Free, open source - cross-platform: runs on Mac, Windows, Linux, et al • https://ccl.northwestern.edu/netlogo Gephi • interactive visualization and exploration platform for networks and complex • systems, dynamic and hierarchical graphs. • Runs on Windows, Linux and Mac OS X. Gephi is open-source and free • http://gephi.github.io/
- 24. Visualization of online groups: data master program WS 14/15 4 days in Nov. 2014 Google+ collection of data by hand
- 25. how individual persons and the group interact one person is more active than the teacher
- 26. Size of nodes according to degree Color according to betweenness master program WS 14/15 3 weeks of online socialisation Interaction in Moodle
- 27. Same group as before Interaction during the whole semester
- 29. training course14/15 size of nodes according to degree color according to betweeness 2 communities
- 30. Conclusion • SNA is complex, has a high potential • I get new insights in my groups - but I don’t understand it entirely until now - there’s a lot of theory behind • I use it to get a quick insight how a group is performing - in my role as convener/moderator