DeepGraph: Multi-Cluster Interactive Visualization of Complex Networks in a Learned Representation Space

Visualizing complex networks with many thousands of nodes and interesting community structure remains a challenging problem. This paper introduces DeepGraph, an off-the-shelf package that takes a network (encoded as an edge-list) as input, and uses open-source packages to interactively visualize nodes in the network in a learned representation space on a web browser. At its core, DeepGraph is powered by established unsupervised node embedding and clustering algorithms, allowing it to operate in an end-to-end fashion without requiring technical expertise or algorithmic parameters. More advanced users can 'swap' out the algorithms in a plug-andplay fashion. DeepGraph is especially designed for non-technical sociologists and subject matter experts looking to explore the data and its community structure before formulating research questions and follow-up studies. We demonstrate the utility and generality of DeepGraph on real-world network datasets spanning domains from digital communication to social media.