Self-Grouping Multi-Network Clustering.
Self-Grouping Multi-Network Clustering.
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DOI：
10.1109/icdm.2016.0146
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Zhang X
中科院分区：
文献类型：
--
作者：
Ni J;Cheng W;Fan W;Zhang X
多个网络的联合聚类已被证明比单独对单个网络执行聚类更准确。针对多网络联合聚类的问题，人们提出了许多多视角、多领域的网络聚类方法。这些方法通常假设所有网络共享一个公共的聚类结构，不同的网络可以提供关于这个底层聚类结构的补充信息。然而，这种假设过于严格，在许多新兴的现实生活中的应用，其中多个网络具有不同的数据分布。更普遍的是，所考虑的网络属于不同的底层群体。只有在同一个底层组中的网络共享相似的聚类结构。通过不同地考虑这些组可以实现更好的集群性能。因此，理想的方法应该能够自动检测网络组，以便同一组中的网络共享共同的聚类结构。为了解决这个问题，我们提出了一种新的方法，ComClus，同时组和集群多个网络。ComClus将节点集群视为网络的特征，并使用它们来区分不同的网络组。网络分组和聚类在学习过程中相互耦合、相互促进。在各种合成和真实的数据集上进行的大量实验验证了该方法的有效性。
Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. Many multi-view and multi-domain network clustering methods have been developed for joint multi-network clustering. These methods typically assume there is a common clustering structure shared by all networks, and different networks can provide complementary information on this underlying clustering structure. However, this assumption is too strict to hold in many emerging real-life applications, where multiple networks have diverse data distributions. More popularly, the networks in consideration belong to different underlying groups. Only networks in the same underlying group share similar clustering structures. Better clustering performance can be achieved by considering such groups differently. As a result, an ideal method should be able to automatically detect network groups so that networks in the same group share a common clustering structure. To address this problem, we propose a novel method, ComClus, to simultaneously group and cluster multiple networks. ComClus treats node clusters as features of networks and uses them to differentiate different network groups. Network grouping and clustering are coupled and mutually enhanced during the learning process. Extensive experimental evaluation on a variety of synthetic and real datasets demonstrates the effectiveness of our method.