Finding community structure in spatially constrained complex networks
文献类型：SCI/SSCI论文
| 作者 | Chen Y.; Xu, J.; Xu, M. Z. |
| 发表日期 | 2015 |
| 关键词 | complex network spatial constraint community modularity image segmentation organization modularity centrality flow |
| 英文摘要 | One feature discovered in the study of complex networks is community structure, in which vertices are gathered into several groups where more edges exist within groups than between groups. Many approaches have been developed for identifying communities; these approaches essentially segment networks based on topological structure or the attribute similarity of vertices, while few approaches consider the spatial character of the networks. Many complex networks are spatially constrained such that the vertices and edges are embedded in space. In geographical space, nearer objects are more related than distant objects. Thus, the relations among vertices are defined not only by the links connecting them but also by the distance between them. In this article, we propose a geo-distance-based method of detecting communities in spatially constrained networks to identify communities that are both highly topologically connected and spatially clustered. The algorithm is based on the fast modularity maximisation (CNM) algorithm. First, we modify the modularity to geo-modularity Q(geo) by introducing an edge weight that is the inverse of the geographic distance to the power of n. Then, we propose the concept of a spatial clustering coefficient as a measure of clustering of the network to determine the power value n of the distance. The algorithm is tested with China air transport network and BrightKite social network data-sets. The segmentation of the China air transport network is similar to the seven economic regions of China. The segmentation of the BrightKite social network shows the regionality of social groups and identifies the dynamic social groups that reflect users' location changes. The algorithm is useful in exploring the interaction and clustering properties of geographical phenomena and providing timely location-based services for a group of people. |
| 出处 | International Journal of Geographical Information Science |
| 卷 | 29 |
| 期 | 6 |
| 页 | 889-911 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1365-8816 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/38419]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Chen Y.,Xu, J.,Xu, M. Z.. Finding community structure in spatially constrained complex networks. 2015. |
入库方式： OAI收割
来源：地理科学与资源研究所
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