Algorithms for clustering data
Algorithms for clustering data
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Substructure discovery using minimum description length principle and background knowledge
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An incremental algorithm for a generalization of the shortest-path problem
Journal of Algorithms
Combinatorial optimization
Fully dynamic output bounded single source shortest path problem
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Internet Routing Architectures, Second Edition
Internet Routing Architectures, Second Edition
Introduction to Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Fully Dynamic Algorithms for Maintaining All-Pairs Shortest Paths and Transitive Closure in Digraphs
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Mining scale-free networks using geodesic clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing and Mining the Citation Graph of the Computer Science Literature
Knowledge and Information Systems
Probabilistic fault localization in communication systems using belief networks
IEEE/ACM Transactions on Networking (TON)
A new approach to dynamic all pairs shortest paths
Journal of the ACM (JACM)
The centrality of pivotal points in the evolution of scientific networks
Proceedings of the 10th international conference on Intelligent user interfaces
Incremental page rank computation on evolving graphs
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Event detection from evolution of click-through data
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
Diagnosis of TCP overlay connection failures using bayesian networks
Proceedings of the 2006 SIGCOMM workshop on Mining network data
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
Experimental analysis of dynamic all pairs shortest path algorithms
ACM Transactions on Algorithms (TALG)
Discovering and Summarising Regions of Correlated Spatio-Temporal Change in Evolving Graphs
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Cluster ranking with an application to mining mailbox networks
Knowledge and Information Systems
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
Bidirectional heuristic search reconsidered
Journal of Artificial Intelligence Research
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs
Intelligent Data Analysis
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Spatio-temporal clustering of road network data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
ciForager: Incrementally discovering regions of correlated change in evolving graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
A query based approach for mining evolving graphs
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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Graphs provide powerful abstractions of relational data, and are widely used in fields such as network management, web page analysis and sociology. While many graph representations of data describe dynamic and time evolving relationships, most graph mining work treats graphs as static entities. Our focus in this paper is to discover regions of a graph that are evolving in a similar manner. To discover regions of correlated spatio-temporal change in graphs, we propose an algorithm called cSTAG. Whereas most clustering techniques are designed to find clusters that optimise a single distance measure, cSTAG addresses the problem of finding clusters that optimise both temporal and spatial distance measures simultaneously. We show the effectiveness of cSTAG using a quantitative analysis of accuracy on synthetic data sets, as well as demonstrating its utility on two large, real-life data sets, where one is the routing topology of the Internet, and the other is the dynamic graph of files accessed together on the 1998 World Cup official website.