An Optimal and Progressive Approach to Online Search of Top-k Influential Communities

Bi, Fei; Chang, Lijun; Lin, Xiao; Zhang, Wenjie

doi:10.48550/arxiv.1711.05857

Cited by 1 publication

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“…Fang et al [23] consider both keyword and structure cohesiveness when searching communities. Li et al [24], [25] and [26], [27] studied the influential community search problem. Zhang et al [28]proposed a novel cohesive subgraph model for attributed graphs, called the (k,r)-core, to capture the cohesiveness of subgraphs from both the graph structure and the vertex attributes.…”

Section: B Social Community Searchmentioning

confidence: 99%

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A Progressive Approach for Neighboring Geosocial Communities Search Over Large Spatial Graphs

Zhang

et al. 2022

IEEE Access

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Searching for neighbors for a query node in a spatial network is a fundamental problem and has been extensively investigated. However, most existing works focus only on the node level when conducting such a query and rarely pay attention to the social relations among the neighbors. We argue that a user, in some cases, is more likely to engage in some activities collectively, i.e., going to the bar with friends rather than alone. For this reason, we consider the neighbor searching problem at a community level in this paper and examine a new problem: Neighboring Geosocial Communities Search (NGCS) over large spatial graphs. Specifically, given a parameter n and query node q, we aim to find the top-n nearest communities for q. Moreover, in each returned community, nodes have cohesive relations with each other and are covered by a minimum covering circle (MCC) whose radius is less than r. It is obvious that the NGCS problem finds its standard applications in marketing and other scenarios but it is very challenging for large spatial graphs because it requires detecting all qualified cohesive user communities. Therefore, in this paper, we adopt a local search approach to reduce the difficulty. The introduced algorithm finds the top-n neighboring geo-social communities through a progressive search in the graph without thoroughly examining the graph. Analyses show that the complexity of the algorithm is decreased by an order of magnitude. Extensive experiments on real social networks confirm the superiority and effectiveness of our solutions.

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Section: B Social Community Searchmentioning

confidence: 99%

“…Next, it creates a vertex list ListV for k-core and sorts the vertices of the eligible k-core in ascending order (Lines 6-7). To sort list X in Line NGCSBasic algorithm is inspired by the research of [18] and [27]. They all used the local search strategy, which searches in the neighborhood of a vertex to find the best community for the vertex.…”

Section: Inputmentioning

confidence: 99%

A Progressive Approach for Neighboring Geosocial Communities Search Over Large Spatial Graphs

Zhang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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An Optimal and Progressive Approach to Online Search of Top-k Influential Communities

Cited by 1 publication

References 0 publications

A Progressive Approach for Neighboring Geosocial Communities Search Over Large Spatial Graphs

A Progressive Approach for Neighboring Geosocial Communities Search Over Large Spatial Graphs

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