Attributed Community Search In Dynamic Networks

2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI)(2018)

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摘要
Recently, community search has been studied a lot. Given a query vertex in a graph, the problem is to find a meaningful community that contains the vertex. It has drawn extensive attention from both academia and industry. However, most of the existing solutions don't take the features of nodes into consideration. And other algorithms, which can be used in attributed graphs, often need the help of index structures. In real life, social networks are usually rapidly evolving entities in which the features of nodes and the structure of the graph may change rapidly over time, and these methods need to constantly update or even rebuild the index. In other words, it is costly and time-consuming. In this paper, we propose an attributed community search (ACS) strategy, which returns a community that satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices have similar keywords with the query node), without the support of index structures (e.g., tree structure). ACS achieves efficient and accurate community search and can be applied in dynamic graphs. The effectiveness and efficiency of our attributed community search strategy is verified by extensive experiments on several real networks with millions of nodes.
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关键词
Community search, Social networks, Attributed graph, Graph mining
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