Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised learning on k-partite graphs
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
Finding tribes: identifying close-knit individuals from employment patterns
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 community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Graph Clustering Using Minimum-Cut Trees
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Clustering of time series data-a survey
Pattern Recognition
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper presents a framework for identifying persistent groups and individuals across multiple time granularities in dynamic graphs. Understanding the longevity of groups and the relevance of individuals within a group is important in many fields, including sociology, biology, economics, psychology, and political science. Different clustering algorithms have been proposed for static and dynamic graphs. However, using the clustering results to understand the changing dynamics of groups can be difficult. In order to better understand how groups evolve and the level of cohesion within these groups, we propose a holistic dynamic clustering framework that allows the user to adjust the underlying algorithms for clustering nodes in a graph that changes over time and then use the final clusters to produce a time hierarchy that highlights the groups and individuals persistent during different time periods. We test our framework and algorithm both on synthetic and real world data. Our findings indicate that our approach not only yields highly accurate results, but also detects unexpected variations in group structure.