论文标题
以优势关系为指导的空间感知的当地社区检测
Spatial-Aware Local Community Detection Guided by Dominance Relation
论文作者
论文摘要
为给定节点找到空间感知社区的问题已在地理社会网络中定义和研究。但是，现有的研究遭受了两个局限性：a）定义社区的标准由难以设定的参数确定； b）算法可能需要全球信息，并且不适合网络不完整的情况。因此，我们提出了空间感知的当地社区检测（SLCD），该发现找到了只有本地信息的空间感知的当地社区，并根据社区内外边缘的稀疏性差异来定义社区。具体来说，为了解决SLCD问题，我们设计了一种基于优势关系的新型空间意识到的本地社区检测算法，但是该算法会造成高成本。为了进一步提高效率，我们提出了近似算法。实验结果表明，所提出的近似算法的表现优于比较算法。
The problem of finding the spatial-aware community for a given node has been defined and investigated in geo-social networks. However, existing studies suffer from two limitations: a) the criteria of defining communities are determined by parameters, which are difficult to set; b) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose an approximate algorithm. Experimental results demonstrate that the proposed approximate algorithm outperforms the comparison algorithms.
