Weighted theta functions and embeddings with applications to Max-Cut, clustering and summarization
Paper i proceeding, 2015

We introduce a unifying generalization of the Lovász theta function, and the associated geometric embedding, for graphs with weights on both nodes and edges. We show how it can be computed exactly by semidefinite programming, and how to approximate it using SVM computations. We show how the theta function can be interpreted as a measure of diversity in graphs and use this idea, and the graph embedding in algorithms for Max-Cut, correlation clustering and document summarization, all of which are well represented as problems on weighted graphs.

Document summarization

Information science

Geometric embedding

Graph embeddings

Embeddings

Correlation clustering

Theta-function

Weighted graph

Semi-definite programming

Författare

Fredrik Johansson

Chalmers, Data- och informationsteknik, Datavetenskap

A. Chattoraj

Indian Institute of Science

University of Rochester

C. Bhattacharyya

Indian Institute of Science

Devdatt Dubhashi

Chalmers, Data- och informationsteknik, Datavetenskap

Advances in Neural Information Processing Systems

10495258 (ISSN)

Vol. 2015-January 1018-1026

29th Annual Conference on Neural Information Processing Systems, NIPS 2015,
Montreal, Canada,

Ämneskategorier

Diskret matematik

Mer information

Senast uppdaterat

2021-07-30