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7 November 2008 Detection of clusters and outlying nodes in spatial networks
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71470K (2008) https://doi.org/10.1117/12.813221
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Construction of network clusters and identifying hub nodes from networks has attracted more and more attentions in spatial network analysis. In this paper, we proposed clustering algorithm and outlying node detection algorithm for spatial road network analysis. Network clustering algorithm consists of constructing clusters and creating a simplified structure of the network. When performing clustering on the network, we introduced the definitions of strong cluster and weak cluster, where each node has more connections within the cluster than with the rest of the graph, for achieving reliable and reasonable clusters. For users' understanding the structure of the network, we constructed a simplified graph approximation of the network, whose nodes were representative nodes in clusters of the network, and edges were the connections between those representative nodes. In outlying node detection algorithm, a node is identified as an outlier, not because of its distribution different from that of other nodes but for its unexpected statistical information. Whether a node is an outlier or not is examined with centrality index. The larger the node has centrality indexes, the more probabilistically it may be identified as an outlier. The experimental results on artificial data sets demonstrated that two algorithms are very efficient and effective.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jiaogen Zhou, Chunjiang Zhao, Jihua Wang, La Qi, and Wenjiang Huang "Detection of clusters and outlying nodes in spatial networks", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470K (7 November 2008); https://doi.org/10.1117/12.813221
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