Flexible and Robust Multi-Network Clustering

Ni, Jingchao; Tong, Hanghang; Wang, Fan; Zhang, Xiang

doi:10.1145/2783258.2783262

Cited by 46 publications

(45 citation statements)

References 24 publications

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“…Our method is also inspired by traditional multi-view and multigraph learning methods [5,16,[24][25][26]41], which aim to integrate multiple data sources in a certain task, such as instance clustering, to obtain performance gain. However, these methods are not designed for multi-network embedding, and none of them uses deep model to exploit the non-linear structures of the network data.…”

Section: Related Workmentioning

confidence: 99%

Co-Regularized Deep Multi-Network Embedding

Chang²,

Liu

et al. 2018

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

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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.

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Section: Related Workmentioning

confidence: 99%

Co-Regularized Deep Multi-Network Embedding

Chang²,

Liu

et al. 2018

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

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“…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]. …”

Section: Related Workmentioning

confidence: 99%

Towards Optimal Connectivity on Multi-Layered Networks

Chen

Bliss

et al. 2017

IEEE Trans. Knowl. Data Eng.

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Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SUBLINE) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in SUBLINE family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

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“…That is, given an ensemble of networks, one aims to identify sets such that networks within a set have similar characteristics. These characteristics, or “features” in this context, can describe any of the following: micro-scale structural properties such as subgraph motifs [11], [12]; multiscale properties such as community structure [13], [14], [15], the spectra of network-related matrices [16] and by defining latent roles [17]. Although clustering layers in a multilayer network is closely related to clustering networks in an ensemble, these are distinct problems with different difficulties and nuances.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], the authors seek to partition a group of networks into subgroups through construction of a network of networks (NoN). Communities in the NoN are chosen such that the networks representing the nodes are sufficiently similar in their underlying community structure.…”

Section: Introductionmentioning

confidence: 99%

Clustering Network Layers with the Strata Multilayer Stochastic Block Model

Stanley

Shai

Taylor

et al. 2016

IEEE Trans. Netw. Sci. Eng.

124

108

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Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the “strata multilayer stochastic block model” (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called “strata”, which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

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Flexible and Robust Multi-Network Clustering

Cited by 46 publications

References 24 publications

Co-Regularized Deep Multi-Network Embedding

Co-Regularized Deep Multi-Network Embedding

Towards Optimal Connectivity on Multi-Layered Networks

Clustering Network Layers with the Strata Multilayer Stochastic Block Model

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