Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Out-of-core coherent closed quasi-clique mining from large dense graph databases
ACM Transactions on Database Systems (TODS)
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient Algorithms for Mining Significant Substructures in Graphs with Quality Guarantees
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Managing and Mining Graph Data
Managing and Mining Graph Data
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
gSketch: on query estimation in graph streams
Proceedings of the VLDB Endowment
Discovery of top-k dense subgraphs in dynamic graph collections
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Many massive web and communication network applications create data which can be represented as a massive sequential stream of edges. For example, conversations in a telecommunication network or messages in a social network can be represented as a massive stream of edges. Such streams are typically very large, because of the large amount of underlying activity in such networks. An important application in these domains is to determine frequently occurring dense structures in the underlying graph stream. In general, we would like to determine frequent and dense patterns in the underlying interactions. We introduce a model for dense pattern mining and propose probabilistic algorithms for determining such structural patterns effectively and efficiently. The purpose of the probabilistic approach is to create a summarization of the graph stream, which can be used for further pattern mining. We show that this summarization approach leads to effective and efficient results for stream pattern mining over a number of real and synthetic data sets.