Truss decomposition of uncertain graphs
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- 作者：Zhaonian Zou ; Rong Zhu
- 关键词：Cohesive subgraph ; Truss ; Truss decomposition ; Truss number ; Uncertain graph
- 刊名：Knowledge and Information Systems
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：50
- 期：1
- 页码：197-230
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Information Systems and Communication Service; IT in Business;
- 出版者：Springer London
- ISSN：0219-3116
- 卷排序：50
文摘
The k-truss of a graph is the largest edge-induced subgraph such that every edge is contained in at least k triangles within the subgraph, where a triangle is a cycle consisting of three vertices. As a new notion of cohesive subgraphs, truss has recently attracted a lot of research attentions in the database and data mining fields. At the same time, uncertainty is an intrinsic property of massive graph data, and truss decomposition (i.e., finding all k-trusses of a graph) has become a key primitive on uncertain graphs. In this paper, we study the truss decomposition problem on uncertain graphs, that is, finding all highly probable k-trusses of an uncertain graph. We first give an formal statement of the truss decomposition problem on uncertain graphs. Then, we prove that the truss decomposition of an uncertain graph attains two elegant properties, namely uniqueness and hierarchy. We show that the truss decomposition of an uncertain graph can be found in \(O(m^{1.5}Q)\) time by proposing an in-memory algorithm called \(\mathtt {TD_{mem}}\), where m is the number of edges of the uncertain graph, and Q is at most the maximum number of common neighbors of the endpoints of an edge. When an uncertain graph is too large to fit into main memory, we propose an external-memory algorithm \(\mathtt {TD_{I/O}}\) to find the truss decomposition of the uncertain graph. Extensive experiments have been carried out to evaluate the practical performance of the proposed algorithms. The experimental results verify that both \(\mathtt {TD_{mem}}\) and \(\mathtt {TD_{I/O}}\) are efficient when an uncertain graph is small enough to fit into main memory, and that \(\mathtt {TD_{I/O}}\) is much faster than \(\mathtt {TD_{mem}}\) when the graph is too large to fit into main memory.