Dec 6, 2021
Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ this process to design a polynomial-time algorithm that approximately finds bipartite components in a hypergraph. We theoretically prove the performance of our proposed algorithm, and compare it against the previous state-of-the-art through extensive experimental analysis. The significance of our work is further demonstrated on several large-scale datasets (Penn Treebank, DBLP, IMDB, and Wikipedia), in which our unsupervised algorithm clearly separates objects of different types (e.g., verbs vs. adverbs, and authors vs. conferences).
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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