Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Bursty Evolution of Blogspace
World Wide Web
AutoPart: parameter-free graph partitioning and outlier detection
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Extraction and classification of dense communities in the web
Proceedings of the 16th international conference on World Wide Web
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Spotting Significant Changing Subgraphs in Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Evolving graphs are used to model the relationship variations between objects in many application domains such as social networks, sensor networks, and telecommunication. In this paper, we study a new problem of discovering burst areas that exhibit dramatic changes during some periods in evolving graphs. We focus on finding the top-k results in a stream of fast graph evolutions. This problem is challenging because when the graph evolutions are coming in a high speed, the solution should be capable of handling a large amount of evolutions in short time and returning the top-k results as soon as possible. The experimental results on real data sets show that our proposed solution is very efficient and effective.