作者
Neil Shah, Danai Koutra, Tianmin Zou, Brian Gallagher, Christos Faloutsos
发表日期
2015/8/10
研讨会论文
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
页码范围
1055-1064
出版商
ACM
简介
How can we describe a large, dynamic graph over time? Is it random? If not, what are the most apparent deviations from randomness -- a dense block of actors that persists over time, or perhaps a star with many satellite nodes that appears with some fixed periodicity? In practice, these deviations indicate patterns -- for example, botnet attackers forming a bipartite core with their victims over the duration of an attack, family members bonding in a clique-like fashion over a difficult period of time, or research collaborations forming and fading away over the years. Which patterns exist in real-world dynamic graphs, and how can we find and rank them in terms of importance? These are exactly the problems we focus on in this work. Our main contributions are (a) formulation: we show how to formalize this problem as minimizing the encoding cost in a data compression paradigm, (b) algorithm: we propose TIMECRUNCH, an …
引用总数
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N Shah, D Koutra, T Zou, B Gallagher, C Faloutsos - Proceedings of the 21th ACM SIGKDD international …, 2015
N Shah, D Koutra, T Zou, B Gallagher, C Faloutsos - Proceedings of the 21th ACM SIGKDD International …, 2015