Joint clustering of multiple networks has been shown to be more accurate than performing clustering on individual networks separately. This is because multi-network clustering algorithms typically assume there is a common clustering structure shared by all networks, and different networks can provide compatible and complementary information for uncovering this underlying clustering structure. However, this assumption is too strict to hold in many emerging applications, where multiple networks usually 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 new method, ComClus, to simultaneously group and cluster multiple networks. ComClus is novel in combining the clustering approach of non-negative matrix factorization (NMF) and the feature subspace learning approach of metric learning. Specifically, it treats node clusters as features of networks and learns proper subspaces from such features to differentiate different network groups. During the learning process, the two procedures of network grouping and clustering are coupled and mutually enhanced. Moreover, ComClus can effectively leverage prior knowledge on how to group networks such that network grouping can be conducted in a semi-supervised manner. This will enable users to guide the grouping process using domain knowledge so that network clustering accuracy can be further boosted. Extensive experimental evaluations on a variety of synthetic and real datasets demonstrate the effectiveness and scalability of the proposed method.

中文翻译：

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ComClus：多网络集群的自分组框架

多个网络的联合聚类已被证明比单独对单个网络进行聚类更准确。这是因为多网络聚类算法通常假设所有网络共享一个共同的聚类结构，而不同的网络可以提供兼容和互补的信息来揭示这种潜在的聚类结构。然而，这种假设过于严格，无法在许多新兴应用程序中成立，其中多个网络通常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。只有同一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聚类性能。因此，理想的方法应该是能够自动检测网络组，使同一组内的网络共享一个共同的聚类结构。为了解决这个问题，我们提出了一种新方法 ComClus，可以同时对多个网络进行分组和聚类。ComClus 在结合非负矩阵分解 (NMF) 的聚类方法和度量学习的特征子空间学习方法方面是新颖的。具体来说，它将节点集群视为网络的特征，并从这些特征中学习适当的子空间以区分不同的网络组。在学习过程中，网络分组和聚类两个过程相互耦合，相互增强。此外，ComClus 可以有效地利用关于如何对网络进行分组的先验知识，从而可以以半监督的方式进行网络分组。这将使用户能够使用领域知识来指导分组过程，从而可以进一步提高网络聚类的准确性。对各种合成和真实数据集的广泛实验评估证明了所提出方法的有效性和可扩展性。