SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The value of strong inapproximability results for clique
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Parallel multilevel k-way partitioning scheme for irregular graphs
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
COBBLER: Combining Column and Row Enumeration for Closed Pattern Discovery
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Extremal Graph Theory
Mining Frequent Closed Patterns in Microarray Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mining closed relational graphs with connectivity constraints
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Mining market data: a network approach
Computers and Operations Research
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Mining near-duplicate graph for cluster-based reranking of web video search results
ACM Transactions on Information Systems (TOIS)
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
On triangulation-based dense neighborhood graph discovery
Proceedings of the VLDB Endowment
TGP: mining top-K frequent closed graph pattern without minimum support
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Mining multi-tag association for image tagging
World Wide Web
Efficient topological OLAP on information networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Content-driven detection of campaigns in social media
Proceedings of the 20th ACM international conference on Information and knowledge management
CP-index: on the efficient indexing of large graphs
Proceedings of the 20th ACM international conference on Information and knowledge management
FUSE: a system for data-driven multi-level functional summarization of protein interaction networks
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Fuse: towards multi-level functional summarization of protein interaction networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Dense subgraph maintenance under streaming edge weight updates for real-time story identification
Proceedings of the VLDB Endowment
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Large scale cohesive subgraphs discovery for social network visual analysis
Proceedings of the VLDB Endowment
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Database research at the National University of Singapore
ACM SIGMOD Record
PLASMA-HD: probing the lattice structure and makeup of high-dimensional data
Proceedings of the VLDB Endowment
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network analysis, from the World Wide Web to stock market analysis. Motivated by this need recently we have seen several new algorithms to tackle this problem based on the (frequent) pattern mining paradigm. A limitation of most of these methods is that they are highly sensitive to parameter settings, rely on exhaustive enumeration with exponential time complexity, and often fail to help the users understand the underlying distribution of components embedded within the host graph. In this article we propose an approximate algorithm, to mine and visualize cohesive subgraphs (dense sub components) within a large graph. The approach, refereed to as Cohesive Subgraph Visualization (CSV) relies on a novel mapping strategy that maps edges and nodes to a multi-dimensional space wherein dense areas in the mapped space correspond to cohesive subgraphs. The algorithm then walks through the dense regions in the mapped space to output a visual plot that effectively captures the overall dense sub-component distribution of the graph. Unlike extant algorithms with exponential complexity, CSV has a complexity of O(V2logV) when fixing the parameter mapping dimension, where V corresponds to the number of vertices in the graph, although for many real datasets the performance is typically sub-quadratic. We demonstrate the utility of CSV as a stand-alone tool for visual graph exploration and as a pre-filtering step to significantly scale up exact subgraph mining algorithms such as CLAN.