Revealing community structures by ensemble clustering using group diffusion

Ivannikova, Elena; Park, Hyun-Woo; Hämäläinen, Timo; Lee, Kichun

doi:10.1016/j.inffus.2017.09.013

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…The main advantage of the CDMEC is that the combination of the deep stacked autoencoder and transfer learning can result in obtaining a low-dimensional feature representation. Compared to strategies which are based on capturing the geometric structure of data using group distances [35], the CDMEC scheme manages to get low-dimensional representations in a more efficient way. Moreover, to reveal more comprehensive similarity relationships between the nodes of a network topology, the CDMEC introduced similarity matrices based to different functions.…”

Section: Ae-based Community Detection Strategiesmentioning

confidence: 99%

A Survey on the Recent Advances of Deep Community Detection

2021

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In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.

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Section: Ae-based Community Detection Strategiesmentioning

confidence: 99%

A Survey on the Recent Advances of Deep Community Detection

2021

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“…In this section, we propose a new measure, called connective dependence (CD), that will lead to a clustering mechanism suitable for data points in a time series. The proposed measure relates to the measure of dependence 19,25,26 that forms the graph connected via Markov chain, but the previous dependence is not suitable for processing large-scale streaming data. CD modifies Markov random walks, including automatic connections to a local representative and thus enabling large-scale clustering.…”

Section: Connective Dependence Clusteringmentioning

confidence: 99%

Online dependence clustering of multivariate streaming data using one‐class SVMs

Lee

2021

Int J of Intelligent Sys

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Online clustering of multivariate streaming data has attracted considerable interest in recent years due to the abundance of data sources. Numerous studies in this field have been performed, but they usually suffer from the practical problems associated with discovering arbitrary‐shaped clusters, specifying major parameters in advance, and detecting aberrant observations. Addressing these issues is important for online‐clustering tasks, where data arrive in continuous streams and group behaviors change simultaneously. In this paper, we propose a kernel‐based online dependence clustering, namely, KODC, that not only estimates the cluster membership using one‐class support vector machines (OC‐SVMs), but also detects outliers distant from the identified clusters by aggregating OC‐SVM decisions in a realtime basis. At the base level, we use a new measure of connective dependence that forms the graph connected via modified Markovian transitions to enable large‐scale clustering. The proposed framework introduces the coherence threshold to extract data points, which can represent a cluster to which they belong, thus controlling the computational complexity without degrading the clustering performance. To track the pattern evolution over time, KODC also updates the classifier configuration maximizing the total group connective dependence. We evaluate this framework on both several synthetic and real‐world data sets involving multivariate streaming data, and compare it experimentally with other popular online‐clustering methods in terms of four evaluation metrics. The results show that our framework effectively identifies the clusters and outliers, especially in various shaped data subject to change over time, without requiring any prior knowledge of the data.

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“…In order to fuse the multiple network data, inspired by the idea of data fusion, we divide the existing methods into four categories: (1) network structural (data-level) fusion methods [15,16]; (2) network feature (feature-level) fusion methods [17,18]; (3) network analysis model (strategy-level) fusion methods [19,20]; and (4) hybrid fusion methods. In this paper, we mainly focus on the former two methods.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Structural Fusion for Multiplex Network

et al. 2020

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Many real-world complex systems have multiple types of relations between their components, and they are popularly modeled as multiplex networks with each type of relation as one layer. Since the fusion analysis of multiplex networks can provide a comprehensive insight, the structural information fusion of multiplex networks has become a crucial issue. However, most of these existing data fusion methods are inappropriate for researchers to apply to complex network analysis directly. The feature-based fusion methods ignore the sharing and complementarity of interlayer structural information. To tackle this problem, we propose a multiplex network structural fusion (MNSF) model, which can construct a network with comprehensive information. It is composed of two modules: the network feature extraction (NFE) module and the network structural fusion (NSF) module. (1) In NFE, MNSF first extracts a low-dimensional vector representation of a node from each layer. Then, we construct a node similarity network based on embedding matrices and K-D tree algorithm. (2) In NSF, we present a nonlinear enhanced iterative fusion (EIF) strategy. EIF can strengthen high-weight edges presented in one (i.e., complementary information) or more (i.e., shared information) networks and weaken low-weight edges (i.e., redundant information). The retention of low-weight edges shared by all layers depends on the tightness of connections of their K-order proximity. The usage of higher-order proximity in EIF alleviates the dependence on the quality of node embedding. Besides, the fused network can be easily exploited by traditional single-layer network analysis methods. Experiments on real-world networks demonstrate that MNSF outperforms the state-of-the-art methods in tasks link prediction and shared community detection.

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Revealing community structures by ensemble clustering using group diffusion

Cited by 15 publications

References 26 publications

A Survey on the Recent Advances of Deep Community Detection

A Survey on the Recent Advances of Deep Community Detection

Online dependence clustering of multivariate streaming data using one‐class SVMs

Nonlinear Structural Fusion for Multiplex Network

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