随着物流网络的快速扩张，如何在异构系统中交换物品信息已经成为影响物流效率的重要因素，而社交网络与物流网络都具有异构的特征，因此将物流网的各个节点看作是社交网络的社区，利用多关系社交网络社区挖掘算法来寻找各个异构的物流网络中固有的社区结构，从而发现物流网中隐藏的规律并进行路径优化等网络行为是可行的。通过对4000例物流数据的对比试验，得出基于相似度的社区挖掘算法在准确率、算法复杂度和效率上都优于K均值算法和回归算法。%With the rapid expansion of logistics network,how to exchange the information of goods in heterogeneous sys⁃tems has become an important factor affecting the efficiency of logistics. However,both social network and logistics network have a same heteroid feature:heterogeneity. Therefore,it is entirely feasible to find out the hidden law of logistics network and optimizing route by using the intrinsic community structure of each heteroid logistics network,which is calculated by a communi⁃ty mining algorithm of multi⁃relation social network. The contrast test result of 4000 logistics data from three different algorithms shows that the community mining algorithm based on similarity is better than K⁃mean algorithm and regression⁃based algorithm in accuracy rate,complexity and efficiency.
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