The Experts below are selected from a list of 94215 Experts worldwide ranked by ideXlab platform
Wenjian Luo - One of the best experts on this subject based on the ideXlab platform.
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Community Detection by Fuzzy Relations
IEEE Transactions on Emerging Topics in Computing, 2020Co-Authors: Wenjian Luo, Zhenglong Yan, Daofu ZhangAbstract:The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering Community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping Community Detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the Community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide Community Detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.
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Local Overlapping Community Detection
ACM Transactions on Knowledge Discovery from Data, 2020Co-Authors: Wenjian Luo, Wenjie Zhu, Bei HuaAbstract:Local Community Detection refers to finding the Community that contains the given node based on local information, which becomes very meaningful when global information about the network is unavailable or expensive to acquire. Most studies on local Community Detection focus on finding non-overlapping communities. However, many real-world networks contain overlapping communities like social networks. Given an overlapping node that belongs to multiple communities, the problem is to find communities to which it belongs according to local information. We propose a framework for local overlapping Community Detection. The framework has three steps. First, find nodes in multiple communities to which the given node belongs. Second, select representative nodes from nodes obtained above, which tends to be in different communities. Third, discover the communities to which these representative nodes belong. In addition, to demonstrate the effectiveness of the framework, we implement six versions of this framework. Experimental results demonstrate that the six implementation versions outperform the other algorithms.
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Multiscale Local Community Detection in Social Networks
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Wenjian Luo, Daofu ZhangAbstract:In real-world social networks, global information (e.g., the number of nodes and the connections between them) is incomplete or expensive to acquire; therefore, local Community Detection becomes especially important. Local Community Detection is used to identify the local Community to which the given starting node belongs according to local information. For a given node, most existing local Community Detection methods can only find single scale local communities but not those of variable sizes. However, local communities with different scales are often required. Therefore, it is necessary and meaningful to find local communities of the given starting node with different scales; we call this multiscale local Community Detection. In this paper, we propose a new local modularity inspired by the global modularity and prove the equivalence of the proposed local modularity with two other typical local modularities. Furthermore, to detect local communities with different scales, we present a method based on the proposed local modularity. We test this method on several synthetic and real datasets, and the experimental results indicate that the detected Community is meaningful and its scale can be changed reasonably.
Daofu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Community Detection by Fuzzy Relations
IEEE Transactions on Emerging Topics in Computing, 2020Co-Authors: Wenjian Luo, Zhenglong Yan, Daofu ZhangAbstract:The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering Community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping Community Detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the Community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide Community Detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.
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Multiscale Local Community Detection in Social Networks
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Wenjian Luo, Daofu ZhangAbstract:In real-world social networks, global information (e.g., the number of nodes and the connections between them) is incomplete or expensive to acquire; therefore, local Community Detection becomes especially important. Local Community Detection is used to identify the local Community to which the given starting node belongs according to local information. For a given node, most existing local Community Detection methods can only find single scale local communities but not those of variable sizes. However, local communities with different scales are often required. Therefore, it is necessary and meaningful to find local communities of the given starting node with different scales; we call this multiscale local Community Detection. In this paper, we propose a new local modularity inspired by the global modularity and prove the equivalence of the proposed local modularity with two other typical local modularities. Furthermore, to detect local communities with different scales, we present a method based on the proposed local modularity. We test this method on several synthetic and real datasets, and the experimental results indicate that the detected Community is meaningful and its scale can be changed reasonably.
Pascal Poncelet - One of the best experts on this subject based on the ideXlab platform.
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Local Community Detection in multilayer networks
Data Mining and Knowledge Discovery, 2017Co-Authors: Roberto Interdonato, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, Pascal PonceletAbstract:The problem of local Community Detection in graphs refers to the identification of a Community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks, in which multiple types of connectivity might be considered. In this work, we fill this gap in the literature by introducing the first framework for local Community Detection in multilayer networks (ML-LCD). We formalize the ML-LCD optimization problem and provide three definitions of the associated objective function, which correspond to different ways to incorporate within-layer and across-layer topological features. We also exploit our framework to generate multilayer global Community structures. We conduct an extensive experimentation using seven real-world multilayer networks, which also includes comparison with state-of-the-art methods for single-layer local Community Detection and for multilayer global Community Detection. Results show the significance of our proposed methods in discovering local communities over multiple layers, and also highlight their ability in producing global Community structures that are better in modularity than those produced by native global Community Detection approaches.
Lijun Sun - One of the best experts on this subject based on the ideXlab platform.
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A Novel Local Community Detection Method Using Evolutionary Computation.
IEEE Transactions on Cybernetics, 2019Co-Authors: Chao Lyu, Yuhui Shi, Lijun SunAbstract:The local Community Detection is a significant branch of the Community Detection problems. It aims at finding the local Community to which a given starting node belongs. The local Community Detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local Community Detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local Community Detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local Community Detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.
Bo Yuan - One of the best experts on this subject based on the ideXlab platform.
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Overlapping Community Detection in signed networks.
Journal of Statistical Mechanics: Theory and Experiment, 2014Co-Authors: Yi Chen, Xiaolong Wang, Bo YuanAbstract:Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community Detection is one of the main challenges for complex network analysis. Most of the existing algorithms for Community Detection in a signed network aim at providing a hard-partition of the network where any node should belong to a Community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping Community Detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft-memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperform other state-of-the-art models at shedding light on the Community Detection in synthetic signed networks.