Graphs and more Complex structures for Learning and Reasoning (GCLR) workshop was held at AAAI 2021. For more details about the workshop, please visit website: https://sites.google.com/view/gclr2021/.
Speaker's Bio: Dr. Philip is a Hedrick Visiting Assistant Adjunct Professor in the Department of Mathematics at UCLA. His research interests include network science, applied probability, and machine learning. In this talk, Phil will talk about his recent work on hypergraph clustering which is based on a generalization of the popular modularity functional for dyadic networks.
Title of the talk: Hypergraph Clustering: From Blockmodels to Modularities
Abstract: Hypergraph clustering is a natural framework for detecting modules of functionally similar entities in complex relational systems. In this talk, we propose a flexible approach to hypergraph clustering based on a generalization of the popular modularity functional for dyadic networks. We first introduce the degree-corrected hypergraph stochastic blockmodel (DCHSBM), a generative model for hypergraphs with heterogenous degree and dimension distributions. We then derive from the DCHSBM likelihood a family of hypergraph clustering objective functions, and derive a combinatorial identity enabling efficient computation of these objectives. Next, we formulate a generalization of the fast Louvain algorithm for finding high-quality partitions. This generalized Louvain algorithm allows us to mine hypergraphs up to 100,000 nodes, without resorting to dyadic projections. We also demonstrate that modularity-based hypergraph clustering can succeed in cases where approaches based on dyadic projections are guaranteed to fail due to information-theoretic limits.
Link for the paper: https://arxiv.org/abs/2101.09611