Weighted theta functions and embeddings with applications to Max-Cut, clustering and summarization

Fredrik Johansson; A. Chattoraj; C. Bhattacharyya; Devdatt Dubhashi

Weighted theta functions and embeddings with applications to Max-Cut, clustering and summarization
Other conference contribution, 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.

Graph embeddings

Weighted graph

Embeddings

Geometric embedding

Theta-function

Document summarization

Information science

Semi-definite programming

Correlation clustering

Author

Fredrik Johansson

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Other publications Research

A. Chattoraj

University of Rochester

Chalmers University of Technology

C. Bhattacharyya

Indian Institute of Science, Bangalore

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Other publications Research

29th Annual Conference on Neural Information Processing Systems, NIPS 2015, Montreal, Canada, 7-12 December

1049-5258 (ISSN)

Vol. 2015-January 1018-1026

Subject Categories (SSIF 2011)

Discrete Mathematics

More information

Created

10/8/2017

Weighted theta functions and embeddings with applications to Max-Cut, clustering and summarization Other conference contribution, 2015