Motif-Driven Graph Analysis

In this talk I will present data-driven algorithms for dense subgraph discovery [11], [16], and community detection [18] respectively. The proposed algorithms leverage graph motifs to attack the large near-clique detection problem, and community detection respectively. In my talk, I will focus on triangles within graphs, but our techniques extend to other motifs as well. The intuition, that has been suggested but not formalized similarly in previous works, is that triangles are a better signature of community than edges. For both problems, we provide theoretical results, we design efficient algorithms, and then show the effectiveness of our methods to multiple applications in machine learning and graph mining.