Biclustering Evolutionary Spatiotemporal Community in Global Trading Network

机译：在全球贸易网络中融合进化的时空共同体

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Detecting evolving communities in dynamic weighted networks are significant for understanding the evolutionary patterns of complex networks. In this paper, a novel algorithm is proposed to detect overlapping evolutionary spatiotemporal communities in the global trading network, a dynamic weighted network. This algorithm is capable of discovering those edges with similar evolving trend in a weighted community, and revealing the evolutionary of nodes and edge weight vectors simultaneously. Experiments on the global trading network show that the proposed algorithm can discover more evolving behaviors and properties which hide in those seemingly stable community structures.

机译：在动态加权网络中检测不断发展的社区对于了解复杂网络的演化模式具有重要意义。本文提出了一种新颖的算法来检测全球交易网络（动态加权网络）中重叠的进化时空群落。该算法能够发现加权社区中具有相似发展趋势的边缘，并同时揭示节点和边缘权重向量的演化。在全球贸易网络上进行的实验表明，该算法可以发现隐藏在那些看似稳定的社区结构中的更多进化行为和特性。

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《International Conference on Cloud Computing and Security》|2017年|589-598|共10页
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Leiming Yan; Zeyu Chen; Ping Zang;
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Link community; Weighted complex network; Community evolution; Biclustering;

机译：链接社区;加权复杂网络;社区发展;集群化;

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Biclustering Evolutionary Spatiotemporal Community in Global Trading Network

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