The document discusses social network analysis using the Gephi software tool. It defines social networks and explains that social network analysis studies network structure and behavior. It also defines networks and graphs. The document then explains Gephi's functions for analyzing networks through measures like degree centrality, community detection algorithms, and network visualization capabilities. It provides steps for importing a network into Gephi and using functions like layout, ranking, filtering, and labeling to visualize communities and analyze the network.

1. Social Network Analysis Using
Gephi
Nilkanth Shet Shirodkar
Mtech – I
14103

2. Social Networks
• A social network is a social structure of
people, related (directly or indirectly) to each
other through a common relation or interest
• Social network analysis (SNA) is the study of
social networks to understand their structure
and behavior

3. Network or Graph ?
• Network often refers to real systems
Web, Socialnetwork, Metabolic network
Language: Network, node, link
• Graph is mathematical representation of a
network
Web graph, Social graph (Facebook or twitter)
Language: Graph, vertex, edge

4. • Communication networks:
Intrusion detection, fraud detection
• Social networks:
• Link prediction, friend recommendation
• Social circle detection
• Social recommendations
• Identify the network of an individual
• community detection

5. Graph types
• Undirected graph
– Facebook friendships
• Directed graph
– Twitter: follow and be followed

6. Gephi
• Gephi is an open source tool designed for the
interactive exploration and visualization of
networks
• Designed to facilitate the user’s exploratory
process through real-time analysis and
visualization
• Visualization module uses a 3D render engine
• Highly scalable can handle over 20,000 nodes

7. Graph measures
• Degree
– In-degree
– Out-degree
• Graph structure measures
– Clustering (global and local)
– Network diameter
• Centrality Measures
– Eigenvector centrality
– PageRank
• Community measures
– Modularity

8. Degree Centrality
• In-Degree : The number of edges entering the
node.
– Size of Node (Mention)
• Out-Degree : The number of edges leaving the
node.
– Twitter User Mention other

9. Statistics
Degree: Calculate the number of links has a node
Degree weighted: Calculates the average number of links
can
have node.
( Degree Distribution, In-Degree Distribution, Out-Degree
Distribution )
Diameter : is the longest distance between two network
nodes.
Closeness centrality: Measures the average distance
between a node and all other nodes.

10. • Density : determines the percentage of network
complementarity.
• Modularity : identifying groupings to highlight the
communities in a network
• Eigenvector centrality: measures the importance of a node
in the network according to its connections
• Related Components: determines the number of connected
components in the network

11. Gephi Processes
• 1. Open
• 2. Layout
• 3. Ranking
• 4. Statistics
• 5. Rank (Modularity, Indegree and Outdegree)
• 6. Layout (Size Adjust)
• 7. Labels
• 8. Community Detection
• 9. Filter
• 10. Label Adjust
• 11. Preview
• 12. Export

12. Gephi: Layout
• From the Layout module on the left side,
choose Force Atlas from the dropdown menu,
then click Run
• Force Atlas makes the connected nodes
attracted to each other and pushes the
unconnected nodes apart to create clusters of
connections
• Click Stop when it seems as if you have some
distinct clusters of nodes

13. Force Atlas : Individual Nodes are Outside and Communities are
coming to center

14. Gephi: Rank(Modularity)
• Ranking in the top left module, and click Choose
a rank parameter from the drop- down such as
Modularity, Indegree and out degree.
• Set Min Size to 10 and Max Size to 150
• Set Min Size to 5 and Max Size to 200
• Min and Max size depends on the nature of your
network.
• Click Apply to change the node sizes according to
their Modularity

15. Modularity Ranking

16. Gephi: Community Detection
• Go back to the Statistics tab on the right and
click Run next to Modularity
• This creates a modularity class value for each
node, which we’ll use to colorize the
communities
• Click Apply to colorize the detected
communities

17. Gephi: Filter
• Go to Filters in the top right module and open
the Library  Partition Count  Modularity
Class
• Filter option basically removes the “leaves” in
the network that are not connected to many
other nodes

18. Gephi: Label Adjust
• The Gephi recommended to run a final layout
adjustment before the export that makes it
easier to read the labels.
• “Label Adjust” works much the same as the
size adjustment, moving the nodes so the
labels are readable

19. Visualized twitter network community
clusters

20. Calculating Basic Network Metrics
Basic network, node, and edge metrics can be calculated using the statistics window.

21. Laying out the Network in Gephi
The network will load in a random cluster of nodes. The first step will be to choose a
layout to make the network more visible.
Choose layout option and select Force Atlas

22. • The Social Network Analysis is a useful and
effective instrument for revealing the main
specificity of the human's relationships of the
social groups.
• Software Gephi is the applicable tool for
visualizing revealed people's interactions and
the relational dimension of the communities
inside the social groups.

23. REFERENCES
• [1] M. Bastian, S. Heymann, and M. Jacomy. Gephi: An open source
software for exploring and manipulating networks. In Proc. 3rd
International Conference on Weblogs and Social Media (ICWSM09), pages
361-362. AAAI, 2009.
• [2] S. Bickel and T. Scheffer. Multi-view clustering. In Proc. 4th IEEE
International Conference on Data Mining (ICDM 04), pages 19-26, 2004.
• [3] D. Cai, Z. Shao, X. He, X. Yan, and J. Han. Mining hidden community in
heterogeneous social networks. In Proc. 3rd International Workshop on
Link Discovery, pages 58-65, 2005.
• [4] C. Ding and X. He. K-nearest-neighbor consistency in data clustering:
Incor- porating local information into global optimization. In Proc. ACM
Symposium on Applied Computing (SAC04), pages 584-589, 2004.
• [5] D. Greene and P. Cunningham. Multi-view clustering for mining
heterogeneous social network data. In Workshop on Information Retrieval
over Social Networks, 31st European Conference on Information Retrieval
(ECIR09), 2009.