Flexible and robust co-regularized multi-domain graph clustering

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

et al. 2018

Self Cite

Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further re ned node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is exible. It allows di erent networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts di erent spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the e ectiveness of our method.

Section: Related Workmentioning

confidence: 99%

Co-Regularized Deep Multi-Network Embedding

Chang²,

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

et al. 2018

Self Cite

“…Graph clustering has been extensively studied in recent years [9][10][11][12][13][14][15][16][17][18][19][20][21]. Shiga et al [9] presented a clustering method which integrates numerical vectors with modularity into a spectral relaxation problem.…”

Section: Related Workmentioning

confidence: 99%

“…GenClus [17] proposed a modelbased method for clustering heterogeneous networks with different link types and different attribute types. CGC [18] is a multidomain graph clustering model to utilize cross-domain relationship as co-regularizing penalty to guide the search of consensus clustering structure. FocusCO [20] solves the problem of finding focused clusters and outliers in large attributed graphs.…”

Section: Related Workmentioning

confidence: 99%

Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis

Zhou

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Buttler

2015

Meta paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents a meta path graph clustering framework, VEPathCluster, that combines meta path vertex-centric clustering with meta path edge-centric clustering for improving the clustering quality of heterogeneous networks. First, we propose an edge-centric path graph model to capture the meta-path dependencies between pairwise path edges. We model a heterogeneous network containing M types of meta paths as M vertex-centric path graphs and M edge-centric path graphs. Second, we propose a clustering-based multigraph model to capture the fine-grained clustering-based relationships between pairwise vertices and between pairwise path edges. We perform clustering analysis on both a unified vertex-centric path graph and each edge-centric path graph to generate vertex clustering and edge clusterings of the original heterogeneous network respectively. Third, a reinforcement algorithm is provided to tightly integrate vertex-centric clustering and edge-centric clustering by mutually enhancing each other. Finally, an iterative learning strategy is presented to dynamically refine both vertex-centric clustering and edge-centric clustering by continuously learning the contributions and adjusting the weights of different path graphs.

“…Network clustering (or graph clustering) [1]–[3] has become an effective means in discovering modules formed by closely related instances in such networks, which may in turn reveal functional structure of the networks. Recently, the attention has moved from clustering in a single homogeneous network (built on instances from one domain) to joint clustering on multiple heterogeneous networks (from different but related domains), due to obvious reasons: integrating information from different but related domains not only may help to resolve ambiguity and inconsistency in clustering outcome, but also may discover and leverage strong associations between clusters from different domains.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the attention has moved from clustering in a single homogeneous network (built on instances from one domain) to joint clustering on multiple heterogeneous networks (from different but related domains), due to obvious reasons: integrating information from different but related domains not only may help to resolve ambiguity and inconsistency in clustering outcome, but also may discover and leverage strong associations between clusters from different domains. Consequently, these multi-view network clustering methods [3], [4] are able to substantially improve the clustering accuracy. For example, millions of genetic variants on human genome have been reported to be disease related, most of which are in the form of single nucleotide polymorphism (SNP).…”

Section: Introductionmentioning

confidence: 99%

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

2015 IEEE International Conference on Data Mining

Cheng²,

Tong

et al. 2015

Self Cite

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.