Clustering with Multiple Graphs

Tang, Wei; Lu, Zhengdong; Dhillon, Inderjit S.

doi:10.1109/icdm.2009.125

Cited by 275 publications

(195 citation statements)

References 9 publications

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“…There have been recent efforts to extend the problem to clustering multiple graphs. For example, Tang et al [17] proposed a linked matrix factorization approach for fusing information from multiple graph sources. However, their approach was designed to cluster multiple graphs in which each graph represents a specific type of proximity relation between the same set of nodes in a given multi-mode network.…”

Section: Introductionmentioning

confidence: 99%

Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks

Mandayam-Comar

Tan

Jain

2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-Identifying cohesive subgroups in networks, also known as clustering is an active area of research in link mining with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. This paper presents a framework that simultaneously clusters nodes from multiple related networks and learns the correspondences between subgroups in different networks. The framework also allows the incorporation of prior information about potential relationships between the subgroups. We have performed extensive experiments on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous clustering over independent clustering of individual networks.

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Section: Introductionmentioning

confidence: 99%

Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks

Mandayam-Comar

Tan

Jain

2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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“…However, it doesn't discriminate target objects from attribute objects, and will be unable to determine objects of which type should be clustered clearly, failing to reduce possible noises. There are other methods dealing with multi-type relations between homogeneous objects, called clustering with multiple graphs (Tang et al 2009a), which have a limited application.…”

Section: Related Workmentioning

confidence: 99%

A novel probabilistic clustering model for heterogeneous networks

Deng

2016

Mach Learn

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Heterogeneous networks, consisting of multi-type objects coupled with various relations, are ubiquitous in the real world. Most previous work on clustering heterogeneous networks either converts them into homogeneous networks or simplifies the modeling of the heterogeneity in terms of specific objects, structures or assumptions. However, few studies consider all relevant objects and relations, and trade-off between integrating relevant objects and reducing the noises caused by relations across objects. In this paper, we propose a general probabilistic graphical model for clustering heterogeneous networks. First, we present a novel graphical representation based on our basic assumptions: different relation types produce different weight distributions to specify intra-cluster probability between two objects, and clusters are formed around cluster cores. Then, we derive an efficient algorithm called PROCESS, standing for PRObabilistic Clustering modEl for heterogeneouS networkS. PROCESS employs a balance-controlled message passing algorithm and mathematical programming for inference and estimation. Experimental results show that our approach is effective and significantly outperforms the state-of-the-art algorithms on both synthetic and real data from heterogeneous networks.

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“…Also, unlike our approach, these works assume a complete bipartite mapping between views. Other multi-view clustering variations are based on cross-modal clustering between perceptual channels (Coen, 2005) and information-theoretic frameworks (Sridharan and Kakade, 2008;Gao et al, 2007;Tang et al, 2009).…”

Section: Multi-view Learningmentioning

confidence: 99%

Multi-view constrained clustering with an incomplete mapping between views

2012

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Abstract. Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multiview algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both singleand multi-view learning scenarios.

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Clustering with Multiple Graphs

Cited by 275 publications

References 9 publications

Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks

Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks

A novel probabilistic clustering model for heterogeneous networks

Multi-view constrained clustering with an incomplete mapping between views

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