| 摘要: |
| 如何发现高质量的社区结构对于深刻研究和分析基于位置的社交网络（location-based social networks，简称LBSN）这种新型复杂网络具有重要意义，然而，现有的面向社交网络的社区发现方法都无法适用于具有多维异构关系的LBSN.为此，提出了一种基于联合聚类的用户社区发现方法Multi-BVD，该方法首先给出了融合用户社交网络与地理位置标签网络中多模实体及其异构关系的社区划分目标函数，然后使用拉格朗日乘子法得到目标函数极小值的迭代更新规则，并运用块值矩阵分解技术来确定最优的社区划分结果.仿真实验结果表明，Multi-BVD方法能够有效地发现LBSN中具有地理特征的用户社区结构，该社区结构在社交关系和地理兴趣标签上都有更优的内聚性，并能更紧密地体现用户社区与地理标签簇间的兴趣关联性. |
| 关键词: 基于位置的社交网络 社区发现 联合聚类 矩阵分解 |
| DOI：10.13328/j.cnki.jos.005269 |
| 分类号:TP311 |
| 基金项目:浙江省自然科学基金（LY13F020026，LY14F020017，LY14C130005）；中国博士后科学基金（2015M581957）；国家自然科学基金（61571400，31471416）；浙江省博士后择优资助科研项目（BSH1502019） |
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| Community Detection of Multi-Dimensional Relationships in Location-Based Social Networks |
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GONG Wei-Hua1, CHEN Yan-Qiang1, PEI Xiao-Bing2, YANG Liang-Huai1
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1.School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;2.School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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| Abstract: |
| How to detect the high-quality community structures in location based social networks (LBSN) plays a significant role that helps to study and analyze this novel type of composite network comprehensively. However, most of existing community detection methods in social networks still cannot solve the problems of combining the correlations of multi-typed heterogeneous relations in LBSN. To address the issue, this paper proposes a co-clustering method for mining the users' community with multi-dimensional relationships, called Multi-BVD. Firstly, the objective function of clustering community is given to fuse multi-modal entities and their multi-dimensional relationships embedded in users' social network and geo-tagged location network. Then, in order to gain the minimum value of the given function, Lagrange multiplier method is applied to obtain the iterative upgrading rules of matrix variants so that the optimal results of users' communities can be determined by the way of decomposing block matrices. Simulation results show that the proposed Multi-BVD can find the community structures with geographical characteristics more effectively and accurately in location based social network. At the same time, the mined non-overlapping community has more cohesive structures in both social relationships and geographical tagged interests, which also can better embody the correlations of interests between users' communities and semantic geo-tagged clusters on locations. |
| Key words: location-based social networks community detection co-clustering matrix decomposition |
