A fast kernel-based multilevel algorithm for graph clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Dynamic Algorithm for Graph Clustering Using Minimum Cut Tree
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Graph Clustering Via a Discrete Uncoupling Process
SIAM Journal on Matrix Analysis and Applications
Transient crowd discovery on the real-time social web
Proceedings of the fourth ACM international conference on Web search and data mining
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We study the problem of automatically identifying ``hotspots'' on the real-time web. Concretely, we propose to identify highly-dynamic ad-hoc collections of users -- what we refer to as crowds -- in massive social messaging systems like Twitter and Facebook. The proposed approach relies on a message-based communication clustering approach over time-evolving graphs that captures the natural conversational nature of social messaging systems. One of the salient features of the proposed approach is an efficient locality-based clustering approach for identifying crowds of users in near real-time compared to more heavyweight static clustering algorithms. Based on a three month snapshot of Twitter consisting of 711,612 users and 61.3 million messages, we show how the proposed approach can efficiently and effectively identify Twitter-based crowds relative to static graph clustering techniques at a fraction of the computational cost.