Effective and Efficient Community Search in Directed Graphs Across Heterogeneous Social Networks

Wang, Zezhong; Yuan, Ye; Zhou, Xiangmin; Qin, Hongchao

doi:10.1007/978-3-030-39469-1_13

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Xu et al [24] studied the problem of multiobjective spatial keyword query with semantics and designed the LIR-tree index to integrate the spatial and semantic information of all objects in a balanced way. Wang et al [25] created a new social network with more complete knowledge and proposed a k-Dcore framework to retrieve effective communities in the directed social network. Chen et al [26] proposed a pivot-based hierarchical indexing structure S 2 R-tree to integrate spatial and semantic information in a seamless way, which carefully designed a space mechanism to transform the high-dimensional semantic vectors to a low-dimensional space so that more effective pruning effect can be achieved.…”

Section: Dominant Nodes Dom(u)mentioning

confidence: 99%

Dominance-Partitioned Subgraph Matching on Large RDF Graph

Ning¹,

SunYunhao²,

ZhaoDeji³

et al. 2020

Complexity

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Subgraph matching on a large graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including question answering and community detection. However, traditional edge-cutting strategy destroys the structure of indivisible knowledge in a large RDF graph. On the premise of load-balancing on subgraph division, a dominance-partitioned strategy is proposed to divide a large RDF graph without compromising the knowledge structure. Firstly, a dominance-connected pattern graph is extracted from a pattern graph to construct a dominance-partitioned pattern hypergraph, which divides a pattern graph as multiple fish-shaped pattern subgraphs. Secondly, a dominance-driven spectrum clustering strategy is used to gather the pattern subgraphs into multiple clusters. Thirdly, the dominance-partitioned subgraph matching algorithm is designed to conduct all isomorphic subgraphs on a cluster-partitioned RDF graph. Finally, experimental evaluation verifies that our strategy has higher time-efficiency of complex queries, and it has a better scalability on multiple machines and different data scales.

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Section: Dominant Nodes Dom(u)mentioning

confidence: 99%