The most persistent soft-clique in a set of sampled graphs

Quadrianto, Novi, Chen, Chao and Lampert, Christoph H (2012) The most persistent soft-clique in a set of sampled graphs. Published in: Langford, John and Pineau, Joelle, (eds.) Proceedings of the 29th International Conference on Machine Learning; Edinburgh, Scotland; 27 June - 3 July 2012. 583-590. Omnipress ISBN 9781450312851

[img]
Preview
PDF - Accepted Version
Download (871kB) | Preview

Abstract

When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data, we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Depositing User: Novi Quadrianto
Date Deposited: 24 Feb 2014 14:33
Last Modified: 16 Jun 2017 14:48
URI: http://sro.sussex.ac.uk/id/eprint/47614

View download statistics for this item

📧 Request an update