Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
Multi-way set enumeration in real-valued tensors
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Discovery of Correlated Sequential Subgraphs from a Sequence of Graphs
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Mining the Temporal Dimension of the Information Propagation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Using graph partitioning to discover regions of correlated spatio-temporal change in evolving graphs
Intelligent Data Analysis
Frequent subgraph discovery in dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Mining useful time graph patterns on extensively discussed topics on the web
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
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ACM Transactions on Intelligent Systems and Technology (TIST)
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PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
ciForager: Incrementally discovering regions of correlated change in evolving graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining rules for rewriting states in a transition-based dependency parser
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Graph mining for object tracking in videos
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A query based approach for mining evolving graphs
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Mining most frequently changing component in evolving graphs
World Wide Web
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current data mining deals with static graphs that do not change over time, coming years will see the advent of an increasing number of time series of graphs. In this article, we investigate how pattern mining on static graphs can be extended to time series of graphs. In particular, we are considering dynamic graphs with edge insertions and edge deletions over time. We define frequency in this setting and provide algorithmic solutions for finding frequent dynamic subgraph patterns. Existing subgraph mining algorithms can be easily integrated into our framework to make them handle dynamic graphs. Experimental results on real-world data confirm the practical feasibility of our approach.