DB Seminar [Spring 2015]: Danai Koutra (Job talk dry run v2.0)

Job talk dry run – round 2

Abstract:

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.