As location-based services using mobile devices have become globally popular these days, social network analysis (especially, community detection) increasingly benefits from combining social relationships with geographic preferences. In this regard, this article addresses the emerging problem of geosocial community detection. We first formalize the problem of geosocial co-clustering , which co-clusters the users in social networks and the locations they visited. Geosocial co-clustering detects higher-quality communities than existing approaches by improving the mapping clusterability , whereby users in the same community tend to visit locations in the same region. While geosocial co-clustering is soundly formalized as non-negative matrix tri-factorization , conventional matrix tri-factorization algorithms suffer from a significant computational overhead when handling large-scale datasets. Thus, we also develop an efficient framework for geosocial co-clustering, called GEOsocial COarsening and DEcomposition (GEOCODE) . To achieve efficient matrix tri-factorization, GEOCODE reduces the numbers of users and locations through coarsening and then decomposes the single whole matrix tri-factorization into a set of multiple smaller sub-matrix tri-factorizations. Thorough experiments conducted using real-world geosocial networks show that GEOCODE reduces the elapsed time by 19–69 times while achieving the accuracy of up to 94.8% compared with the state-of-the-art co-clustering algorithm. Furthermore, the benefit of the mapping clusterability is clearly demonstrated through a local expert recommendation application.

中文翻译：

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地缘社会共聚

如今，随着使用移动设备的基于位置的服务在全球范围内流行，社交网络分析（尤其是社区检测）越来越受益于将社交关系与地理偏好相结合。在这方面，本文解决了地理社会社区检测的新兴问题。我们首先将问题形式化地缘社会共聚，它将社交网络中的用户和他们访问的位置共同聚集在一起。Geosocial co-clustering 通过改进映射集群性，即同一社区中的用户倾向于访问同一地区的位置。虽然地缘社会共聚类被合理地形式化为非负矩阵三分解，传统的矩阵三因子分解算法在处理大规模数据集时会遭受巨大的计算开销。因此，我们还开发了一个有效的地理社会共聚类框架，称为GEOsocial 粗化和分解 (GEOCODE). 为了实现高效的矩阵三分解，GEOCODE 通过粗化减少用户和位置的数量，然后将单个全矩阵三分解分解为一组多个较小的子矩阵三分解。使用真实世界的地理社会网络进行的彻底实验表明，与最先进的共聚类算法相比，GEOCODE 将经过时间减少了 19-69 倍，同时实现了高达 94.8% 的准确度。此外，通过本地专家推荐应用程序清楚地证明了映射集群性的好处。