Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment

Abstract

Network clustering is an important problem that has recently drawn a lot of attentions. Most existing work focuses on clustering nodes within a single network. In many applications, however, there exist multiple related networks, in which each network may be constructed from a different domain and instances in one domain may be related to instances in other domains. In this paper, we propose a robust algorithm, MCA, for multi-network clustering that takes into account cross-domain relationships between instances. MCA has several advantages over the existing single network clustering methods. First, it is able to detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networks by leveraging the duality between clustering individual networks and inferring cross-network cluster alignment. Finally, it provides a multi-network clustering solution that is more robust to noise and errors. We perform extensive experiments on a variety of real and synthetic networks to demonstrate the effectiveness and efficiency of MCA.

Towards Optimal Connectivity on Multi-Layered Networks

“…In [47], a collaborative filtering based method is proposed to infer the missing cross-layer dependencies in multi-layered network. Other remotely related studies include cross-network ranking [48] <au>and clustering [49], [50] <au>in multi-layered networks, and multi-view data analysis [51], [52], [53]. …”

Self-Grouping Multi-network Clustering

“…In these methods, views can be either networks or data-feature matrices of the same set of objects. Recent methods on multi-domain network clustering [6], [7] integrate networks of different sets of objects by cross-network object mapping relationships. Ensemble clustering [12] does not simultaneously clustering multiple data views, but aims to find an agreement of individual clustering results.…”

“…Note that we focus on finding non-overlapping clusters, which is also the common setting of the existing multi-view (domain) network clustering methods [1], [3]–[7]. …”

“…Different views can provide complementary information on the underlying data distribution. Multi-domain network clustering [6], [7] integrates networks of different sets of objects, and uses mappings between objects in different networks to penalize inconsistent clusterings.…”

“…This is a novel and non-trivial challenge. The existing multi-network clustering methods either assume all clusters are common [3]–[5] or simply enhance common clusters without identifying them [1], [6], [7] thus cannot tackle this problem.…”