Approximating clique is almost NP-complete (preliminary version)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient approximation algorithms for semidefinite programs arising from MAX CUT and COLORING
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computing Frequent Graph Patterns from Semistructured Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Convex Optimization
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Support measures for graph data*
Data Mining and Knowledge Discovery
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
An SDP primal-dual algorithm for approximating the Lovász-theta function
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
All normalized anti-monotonic overlap graph measures are bounded
Data Mining and Knowledge Discovery
Approximating Semidefinite Packing Programs
SIAM Journal on Optimization
On the Shannon capacity of a graph
IEEE Transactions on Information Theory
Nearly exact mining of frequent trees in large networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Graph support measures are functions measuring how frequently a given subgraph pattern occurs in a given database graph. An important class of support measures relies on overlap graphs. A major advantage of the overlap graph based approaches is that they combine anti-monotonicity with counting occurrences of a pattern which are independent according to certain criteria. However, existing overlap graph based support measures are expensive to compute. In this paper, we propose a new support measure which is based on a new notion of independence. We show that our measure is the solution to a linear program which is usually sparse, and using interior point methods can be computed efficiently. We show experimentally that for large networks, in contrast to earlier overlap graph based proposals, pattern mining based on our support measure is feasible.