---
res:
  bibo_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.\r\n    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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Novi
      foaf_name: Quadrianto, Novi
      foaf_surname: Quadrianto
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Chao
      foaf_name: Chen, Chao
      foaf_surname: Chen
      foaf_workInfoHomepage: http://www.librecat.org/personId=3E92416E-F248-11E8-B48F-1D18A9856A87
  dct_date: 2012^xs_gYear
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: The most persistent soft-clique in a set of sampled graphs@
...