Social Network Analysis and Visualization with Neo4j

Social network analysis is the process of investigating social structures through the use of network and graph theories. By applying graph algorithms to social networks, communication networks, and collaboration networks we can draw insight from the network to answer questions such as:

Who is the most influential person in the network?
Is fraudulent activity going on in the network?
What are the communities in the network?
Over what topics do members of the network exert influence?

This presentation will introduce graphs and graph databases with a focus on using Neo4j to apply social network analysis techniques to real world graph datasets. We will see how to implement and run graph algorithms such as PageRank, centralities measures, pathfinding, clustering algorithms, and topic analysis. We will demonstrate how to use these algorithms from Neo4j and also how to use Python data science tools with Neo4j.

Visualization is also an important component of social network analysis as the algorithms are useless without being able to visualize and interpret the results. Graph data visualization is often more complex than tabular data visualization. We will also discuss and demonstrate visualization techniques for our graph datasets to show how to apply the results of social network analysis algorithms.

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