Fast discovery of association rules
Advances in knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
A new trust region technique for the maximum weight clique problem
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
gApprox: Mining Frequent Approximate Patterns from a Massive Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
GADDI: distance index based subgraph matching in biological networks
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining the Temporal Dimension of the Information Propagation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Towards proximity pattern mining in large graphs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Assessing the strength of structural changes in cooccurrence graphs
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
REX: explaining relationships between entity pairs
Proceedings of the VLDB Endowment
BiQL: a query language for analyzing information networks
Bisociative Knowledge Discovery
Review of bisonet abstraction techniques
Bisociative Knowledge Discovery
An efficiently computable support measure for frequent subgraph pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Approximate graph mining with label costs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent neighborhood patterns in a large labeled graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Frequent subgraph summarization with error control
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Weighted path as a condensed pattern in a single attributed DAG
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mining closed patterns in relational, graph and network data
Annals of Mathematics and Artificial Intelligence
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
Mining spatiotemporal patterns in dynamic plane graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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The standard, transactional setting of pattern mining assumes that data is subdivided in transactions; the aim is to find patterns that can be mapped onto at least a minimum number of transactions. However, this setting can be hard to apply when the aim is to find graph patterns in databases consisting of large graphs. For instance, the web, or any social network, is a single large graph that one may not wish to split into small parts. The focus in network analysis is on finding structural regularities or anomalies in one network, rather than finding structural regularities common to a set of them. This requires us to revise the definition of key concepts in pattern mining, such as support, in the single-graph setting. Our contribution is a support measure that we prove to be computationally less expensive and often closer to intuition than other measures proposed. Further we prove several properties between these measures and experimentally validate the efficiency of our measure.