---
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: Omnipress@
dct_title: The most persistent soft-clique in a set of sampled graphs@
...