Algorithms for clustering data
Algorithms for clustering data
Introduction to algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A generalized framework for mining spatio-temporal patterns in scientific data
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
A new Mallows distance based metric for comparing clusterings
ICML '05 Proceedings of the 22nd international conference on Machine learning
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
A Simple Model for Sequences of Relational State Descriptions
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Incremental Clustering of Mobile Objects
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs
Intelligent Data Analysis
Balancing Graph Voronoi Diagrams
ISVD '09 Proceedings of the 2009 Sixth International Symposium on Voronoi Diagrams
Data Mining and Knowledge Discovery
Periodic subgraph mining in dynamic networks
Knowledge and Information Systems
Analysis of large multi-modal social networks: patterns and a generator
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Mining Heavy Subgraphs in Time-Evolving Networks
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Data mining techniques for understanding how graphs evolve over time have become increasingly important. Evolving graphs arise naturally in diverse applications such as computer network topologies, multiplayer games and medical imaging. A natural and interesting problem in evolving graph analysis is the discovery of compact subgraphs that change in a similar manner. Such subgraphs are known as regions of correlated change and they can both summarise change patterns in graphs and help identify the underlying events causing these changes. However, previous techniques for discovering regions of correlated change suffer from limited scalability, making them unsuitable for analysing the evolution of very large graphs. In this paper, we introduce a new algorithm called ciForager, that addresses this scalability challenge and offers considerable improvements. The efficiency of ciForager is based on the use of new incremental techniques for detecting change, as well as the use of Voronoi representations for efficiently determining distance. We experimentally show that ciForager can achieve speedups of up to 1000 times over previous approaches. As a result, it becomes feasible for the first time to discover regions of correlated change in extremely large graphs, such as the entire BGP routing topology of the Internet.