Mining the collaboration patterns on social networks has been studied extensively in recent years. Collaboration patterns are manners of how individuals collaborate with each other, and such patterns can be represented by graph substructures. In some existing studies, including frequent subgraph pattern mining, only the structure pattern is considered, and a minimum support should be given for controlling the scale of results. In some cases, interesting patterns could not be frequent, and exactly matching between patterns and communities is also unnecessary. We consider the social positions of community members, and give a pattern specification on weighted graphs. We propose a similarity-based pattern matching measure, and our goal is to enumerate all the representative collaboration patterns based on that. We design a distance-based clustering method to retrieve collaboration patterns, and we verify our algorithms on a large real data set.%社会网络上的模式挖掘是近年来的研究热点之一,合作模式是社会网络上个体间的合作方式,这种模式可以通过社会网络的子结构表示.已有的基于频繁模式的挖掘算法主要考虑合作关系的结构特征,并且往往需要给定支持度阈值来控制结果的规模.在本文中,我们认为社会网络中的模式不一定需要是频繁的,模式与社区也并不需要精确匹配.我们在合作模式中考虑节点的社会地位,并在加权图上给出了一种模式的定义方法,和一种基于互相似性的模式匹配衡量标准,目的在于找出网络中具有“代表性”的合作模式.我们设计了一种基于距离的聚类方法用于抽取这种模式,并在一个大规模的真实数据集上进行了验证.
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