On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
What's really new on the web?: identifying new pages from a series of unstable web snapshots
Proceedings of the 15th international conference on World Wide Web
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
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 community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Spotting Significant Changing Subgraphs in Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Fast Method to Mine Frequent Subsequences from Graph Sequence Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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Discovery of evolving regions in large graphs is an important issue because it is the basis of many applications such as spam websites detection in the Web, community lifecycle exploration in social networks, and so forth. In this paper, we aim to study a new problem, which explores the evolution process between two historic snapshots of an evolving graph. A formal definition of this problem is presented. The evolution process is simulated as a fire propagation scenario based on the Forest Fire Model (FFM) [17]. We propose two efficient solutions to tackle the issue which are grounded on the probabilistic guarantee. The experimental results show that our solutions are efficient with regard to the performance and effective on the well fitness of the major characteristics of evolving graphs.