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On Efficient Large Maximal Biplex Discovery
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DOI：10.1109/TKDE.2021.3077071.png)
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摘要
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Cohesive subgraph discovery is an important problem in bipartite graph mining. In this paper, we focus on one kind of cohesive structure, called $k$k-biplex, where each vertex of one side is disconnected from at most $k$k vertices of the other side. We consider the large maximal $k$k-biplex enumeration problem which is to list all those maximal $k$k-biplexes with the number of vertices at each side at least a non-negative integer $\theta$theta. This formulation, we observe, has various applications and targets to find non-redundant results by excluding non-maximal ones. Existing approaches suffer from massive redundant computations and can only run on small and moderate datasets. Towards improving scalability, we propose an efficient tree-based algorithm with two advanced strategies and powerful pruning techniques. Experimental results on real and synthetic datasets show the superiority of our algorithm over existing approaches.
Keyword:
Bipartite graph
large maximal biplex
graph mining
maximal subgraph enumeration
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