Whats in My Data? Fast, Principled Algorithms for Exploring Large Graphs

Networks naturally capture a host of real-world interactions, spanning from friendships to brain activity. But, given a massive graph, such as the Facebook social network, what can be learned about its structure? Are there any changes over time? Where should people's attention be directed? In this talk I will present my work on scalable algorithms that help us to explore and make sense of large, networked data when we want to know "what's in the data". I will present how summarization and similarity analysis can help answer this question, and I will focus on two of my approaches "VoG" and "DeltaCon". VoG disentangles the complex graph connectivity patterns, and efficiently summarizes large graphs with important and semantically meaningful structures by leveraging information theoretic methods. DeltaCon is a well-founded, fast method that detects and explains changes in time-evolving or aligned networks by assessing their similarity. Both works are being used by industry, and give interesting discoveries in large real-world graphs.
Danai Koutra is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon. She earned her M.S. from CMU in 2013 and her diploma in ECE at the National Technical University of Athens in 2010. She works on large-scale graph mining and devises algorithms and methods for exploring, understanding, and learning from graph data when the nature of the problem is not known in advance. She holds one "rate-1" patent, and has six (pending) patents on bipartite graph alignment. She also has many papers (including 2 award-winning papers) and tutorials in top data mining conferences. Her work has been covered by media outlets, such as the MIT Technology Review, and is being taught in courses at top universities, including the Tepper School of Business at CMU and Rutgers University.