Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

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》资源》论文》Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

2020-02-04 |

85 |

25 |

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
论文

Abstract

We introduce a unifying generalization of the Lovasz 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.

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