conference paper
The most persistent soft-clique in a set of sampled graphs
published
yes
Novi
Quadrianto
author
Christoph
Lampert
author 40C20FD2-F248-11E8-B48F-1D18A9856A870000-0001-8622-7887
Chao
Chen
author 3E92416E-F248-11E8-B48F-1D18A9856A87
ChLa
department
HeEd
department
ICML: International Conference on Machine Learning
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.
Omnipress2012Edinburgh, United Kingdom
eng
Proceedings of the 29th International Conference on Machine Learning
211-218
N. Quadrianto, C. Lampert, and C. Chen, “The most persistent soft-clique in a set of sampled graphs,” in <i>Proceedings of the 29th International Conference on Machine Learning</i>, Edinburgh, United Kingdom, 2012, pp. 211–218.
Quadrianto N, Lampert C, Chen C. 2012. The most persistent soft-clique in a set of sampled graphs. Proceedings of the 29th International Conference on Machine Learning. ICML: International Conference on Machine Learning 211–218.
Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique in a set of sampled graphs. In <i>Proceedings of the 29th International Conference on Machine Learning</i> (pp. 211–218). Edinburgh, United Kingdom: Omnipress.
N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International Conference on Machine Learning, Omnipress, 2012, pp. 211–218.
Quadrianto, Novi, Christoph Lampert, and Chao Chen. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” In <i>Proceedings of the 29th International Conference on Machine Learning</i>, 211–18. Omnipress, 2012.
Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set of sampled graphs. In: <i>Proceedings of the 29th International Conference on Machine Learning</i>. Omnipress; 2012:211-218.
Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” <i>Proceedings of the 29th International Conference on Machine Learning</i>, Omnipress, 2012, pp. 211–18.
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