Shweta Jain

Thursday, May 3th, 2018, 11h, salle 26-00/101, Campus Jussieu

Large graphs have become commonplace making many of the traditional graph-theoretic algorithms infeasible. Moreover, sometimes we don’t have access to the whole graph. This has led us to revisit graph algorithms and necessitated graph sampling. In this talk, I will explore 2 applications of graph sampling. In the first application, called TurnShadow, we propose a method for efficient counting of k-cliques. It uses the classic result called Turn’s theorem to give provable error bounds. We also do extensive evaluation of the method on real-world graph instances and demonstrate that it is fast and extremely accurate. In the second application, we propose a method called SADDLES to estimate the degree distribution when we are given only limited access to the graph, and accessing the graph is costly. We assume we have access to uniform at random (u.a.r ) vertex, u.a.r neighbor of a vertex and the degree of the vertex. We compare SADDLES with many other state-of-the-art methods and demonstrate that SADDLES requires far fewer samples to achieve the same degree of accuracy.