Solving graph compression via optimal transport

Vikas K. Garg, Tommi Jaakkola

Tutkimustuotos: LehtiartikkeliConference articleScientificvertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation can be setup for either directed or undirected graphs, and its dual characterization is cast in terms of distributions over the nodes. The compression pertains to the support of node distributions and makes the problem challenging to solve directly. To this end, we introduce Boolean relaxations and specify conditions under which these relaxations are exact. The relaxations admit algorithms with provably fast convergence. Moreover, we provide an exact O(d log d) algorithm for the subproblem of projecting a d-dimensional vector to transformed simplex constraints. Our method outperforms state-of-the-art compression methods on graph classification.

AlkuperäiskieliEnglanti
JulkaisuADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
Vuosikerta32
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaConference on Neural Information Processing Systems - Vancouver, Kanada
Kesto: 8 joulukuuta 201914 joulukuuta 2019
Konferenssinumero: 33
https://neurips.cc

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