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Community detection in signed networks by relaxing modularity optimization with orthogonal and nonnegative constraints

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 10645-10654页

作者： Zhang, Yunfei Liu, Yuyan Ma, Xiaomeng Song, Jie Kunming Univ Sch Informat Engn Kunming 650214 Yunnan Peoples R China Shenzhen Univ Coll Econ Shenzhen 518060 Peoples R China

Community detection in networks including singed edges is a primary challenge that has already attracted substantial attention. In this paper, we show that this task could be reformulated as a combinatorial optimization concerning the trace of the signed modularity matrix. Keeping the orthogonal and nonnegative constraints in the relaxation, we propose a multiplicative update rule, named the SMON algorithm, which results in a solution that is a close approximation to the genuine community indication matrix. In addition, the rows of the solution can be referred to as the probabilities of corresponding vertex falling into each community, which can help us to discover the overlapping community structure of the network and identify vertices that reside on the watersheds between different communities. Experimental results on real-life social networks as well as synthetic signed networks verify that our method is effective and superior to the existing approaches.

关键词： modularity optimization Community detection Relaxed algorithm Orthogonal and nonnegative constraints Signed networks

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A Metaheuristic-Based modularity optimization Algorithm Driven by Edge Directionality for Directed Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3804-3817页

作者： Xiao, Jing Zou, Yu-Cheng Xu, Xiao-Ke Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Dalian Minzu Univ SEAC Key Lab Big Data Appl Technol Dalian 116600 Peoples R China Beijing Normal Univ Computat Commun Res Ctr Beijing 100875 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Community detection of directed networks has been demonstrated to be able to reveal network function and dynamic characteristics, due to the special information brought by asymmetric edge directionality, but the critical issue of improving the quality of detected communities has not been fully addressed. In this study, we propose a metaheuristic-based modularity optimization method for directed network community detection (MMDCD), which comprehensively leverages nature-inspired metaheuristics and edge directionality information. Specifically, a nature-inspired metaheuristic algorithm, termed diversified farmland fertility (DFF), is improved to provide an efficient optimization strategy for directed modularity maximization. More importantly, the information induced by edge directionality, including edge density, edge pattern, and node importance, are fully used to improve the quality of optimized partitions. Experimental results on synthetic and real-life networks prove the effectiveness of MMDCD, which is superior to state-of-the-art community detection algorithms in terms of quality and accuracy.

关键词： Directed network community detection modularity optimization edge directionality nature-inspired metaheuristics

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KO: modularity optimization in community detection

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 11073-11087页

作者： Oztemiz, Furkan Karci, Ali Inonu Univ Dept Software Engn TR-44210 Malatya Turkiye

Many algorithms have been developed to detect communities in networks. The success of these developed algorithms varies according to the types of networks. A community detection algorithm cannot always guarantee the best results on all networks. The most important reason for this is the approach algorithms follow when dividing any network into communities (sub-networks). The modularity of the network determines the quality of communities in networks. It is concluded that networks with high modularity values are divided into more successful communities (clusters, sub-networks). This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The basic approach of the proposed algorithm is to transfer nodes at the community boundary to neighboring communities if they meet the specified conditions. The method called KO (Karci-Oztemiz) optimization algorithm maximizes the modularity value of any community detection algorithm in the best case, while it does not change the modularity value in the worst case. For the KO algorithm's test, in this study, Walktrap, Cluster Edge Betweenness, Label Propagation, Fast Greedy, and Leading Eigenvector community detection algorithms have been applied on three popular networks that were unweighted and undirected previously used in the literature. The community structures created by five community detection algorithms were optimized via the KO algorithm and the success of the proposed method was analyzed. When the results are examined, the modularity values of the community detection algorithms applied on the three different networks have increased at varying rates (0%,.,14.73%).

关键词： Community modularity Community detection algorithms Graph network modularity optimization

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A Framework for Robust Online Video Contrast Enhancement Using modularity optimization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2012年第9期22卷 1266-1279页

作者： Choudhury, Anustup Medioni, Gerard Univ So Calif Los Angeles CA 90089 USA

We address the problem of video contrast enhancement. Existing techniques either do not exploit temporal information at all or do not exploit it correctly. This results in inconsistency that causes undesirable flash and flickering artifacts. Our method analyzes video streams and cluster frames that are similar to each other. Our method does not have omniscient information about the entire video sequence. It is an online process with a fixed delay. A sliding window mechanism successfully detects shot boundaries "on-the-fly" in a video. A graph-based technique called "modularity" performs automatic clustering of video frames without a priori information about clusters. For every cluster in the video, we extract key frames belonging to each cluster using eigen analysis and estimate enhancement parameters for only the key frame, then use these parameters to enhance frames belonging to that cluster, thus making our method robust. We evaluate the clustering method on video sequences from the TRECVid 2001 dataset and compare it with existing methods. We show reduction of flash artifacts in enhanced videos. We show statistically significant improvement in perceived video quality and validate that by conducting experiments on human observers. We show application of our clustering process to perform robust video segmentation.

关键词： Human validation modularity optimization shot detection video enhancement video segmentation

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Higher Order Fuzzy Membership in Motif modularity optimization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 7143-7156页

作者： Xiao, Jing Wei, Ya-Wei Cao, Jing Xu, Xiao-Ke Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Higher order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-world networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher order fuzzy community information. This study introduces a novel concept of higher order fuzzy memberships that quantify the membership grades of motifs to crisp higher order communities, thereby revealing partial community affiliations. Furthermore, we utilize higher order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships-assisted motif-based evolutionary modularity. On the one hand, a fuzzy membership-based neighbor community modification strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging strategy is proposed to combine excessively fragmented communities, enhancing local search ability. Experimental results indicate that the proposed framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in networks with ambiguous and complex structures.

关键词： optimization Topology Network motifs Bridges Accuracy Partitioning algorithms Image edge detection Fuzzy systems Clustering algorithms Space exploration Fuzzy membership higher order community detection (HCD) modularity optimization network motif

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Partition-Merge: Distributed Inference and modularity optimization

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IEEE ACCESS 2021年 9卷 54032-54055页

作者： Blondel, Vincent Jung, Kyomin Kohli, Pushmeet Shah, Devavrat Won, Seungpil Catholic Univ Louvain Appl Math B-1348 Louvain La Neuve Belgium Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea DeepMind London N1C 4AG England MIT 77 Massachusetts Ave Cambridge MA 02139 USA Seoul Natl Univ Automat & Syst Res Inst Seoul 08826 South Korea

This paper presents a novel meta-algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our novel randomized partitioning scheme, runs the centralized algorithm on each partition separately, and then stitches the resulting solutions to produce a global solution. We demonstrate the efficiency of the PM algorithm on two popular problems: computation of Maximum A Posteriori (MAP) assignment in an arbitrary pairwise Markov Random Field (MRF) and modularity optimization for community detection. We show that the resulting distributed algorithms for these problems become fast, which run in time linear in the number of nodes in the graph. Furthermore, PM leads to performance comparable or even better to that of the centralized algorithms as long as the graph has polynomial growth property. More precisely, if the centralized algorithm is a C factor approximation with constant C > 1, the resulting distributed algorithm is a (C + delta)-factor approximation for any small delta > 0;and even if the centralized algorithm is a non-constant (e.g., logarithmic) factor approximation, then the resulting distributed algorithm becomes a constant factor approximation. For general graphs, we compute explicit bounds on the loss of performance of the resulting distributed algorithm with respect to the centralized algorithm. To show the efficiency of our algorithm, we conducted extensive experiments both on real-world networks and on synthetic networks. The experiments demonstrate that the PM algorithm provides a good trade-off between accuracy and running time.

关键词： Approximate MAP graphical model modularity optimization partition

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A Novel Dynamic Community Detection Algorithm Based on modularity optimization 7

A Novel Dynamic Community Detection Algorithm Based on Modul...

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Meng, Xiangfeng Tong, Yunhai Liu, Xinhai Zhao, Shuai Yang, Xianglin Tan, Shaohua Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9781467399043

Dynamic community detection has been an attractive topic due to its ability to reveal the evolutionary trends over time. However, existing dynamic community detection algorithms suffer from several disadvantages. Some make strong assumptions about the generation of communities, or require priori knowledge. In this paper, we propose a novel algorithm, dynamic Louvain method, to detect communities in temporal networks based on modularity optimization. The basic motivation is that the communities across different time steps should smoothly evolve. When partitioning temporal networks at a given time step, we should take historical network structure into consideration. Besides, this algorithm makes no assumption about the generation of communities, and is able to decide the number of communities automatically. This novel algorithm is applied to the temporal financial networks, and numerical evaluations show that this novel algorithm could obtain better partitions, compared with other state-of-art algorithms.

关键词： Dynamic community detection dynamic Louvain method modularity optimization

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An Immunological Algorithm for Graph modularity optimization 19th

An Immunological Algorithm for Graph Modularity Optimization

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Spampinato, A. G. Scollo, R. A. Cavallaro, S. Pavone, M. Cutello, V Univ Catania Dept Math & Comp Sci Vle A Doria 6 I-95125 Catania Italy Italian Natl Res Council Inst Biomed Reasearch & Innovat Via P Gaifami 18 I-95126 Catania Italy

ISBN: (纸本)9783030299330;9783030299323

Complex networks constitute the backbone of complex systems. They represent a powerful interpretation tool for describing and analyzing many different kinds of systems from biology, economics, engineering and social networks. Uncovering the community structure exhibited by real networks is a crucial step towards a better understanding of complex systems, revealing the internal organization of nodes. However, existing algorithms in the literature up-to-date present several crucial issues, and the question of how good an algorithm is, with respect to others, is still open. Recently, Newman [18] suggested modularity as a natural measure of the goodness of network community decompositions. Here we propose an implementation of an Immunological Algorithm, a population based computational systems inspired by the immune system and its features, to perform community detection on the methods of modularity maximization. The reliability and efficiency of the proposed algorithm has been validating by comparing it with Louvain algorithm one of the fastest and the popular algorithm based on a multiscale modularity optimization scheme.

关键词： Immunological-inspired algorithms Networks modularity optimization Community structure Opt-IA

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Fraudster Detection Based on modularity optimization Algorithm 23

Fraudster Detection Based on Modularity Optimization Algorit...

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IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Luan, Tingting Yan, Zhongmin Zhang, Shidong Shandong Univ Sch Comp Sci & Technol Jinan Shandong Peoples R China

ISBN: (纸本)9781728103501

With the advancement and popularization of healthcare informatization, healthcare fraud has happened frequently. Doctors, patients and pharmacies are involved, and these various subjects cooperate with each other to form a complicated fraud relationship. Healthcare fraud has caused great damage to the safety of the health insurance fund. However, fraudster detection problem has not been solved properly at present. Therefore, Fraudster Detection Based on modularity optimization Algorithm (FDMOA) approach is developed to find doctor fraudsters in a good accuracy, which models the medical insurance records as a heterogeneous weighted network of doctors and medicines, and uses a modularity optimization algorithm to divide doctors and drugs into corresponding communities on a heterogeneous network. Finally, anomaly detection strategy was used in the community to find an abnormal doctor. Experiments show that the proposed method outperforms distance-based, clustering-based, and LOF algorithms.

关键词： Community Division Fraudster Detection Heterogeneous Network modularity optimization

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Enhancing modularity optimization via Local Close-Knit Structures 14

Enhancing Modularity Optimization via Local Close-Knit Struc...

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14th International Conference on Web Information Systems Engineering (WISE)

作者： Zhang, Huajian Wang, Youquan Fang, Changjian Wu, Zhiang Nanjing Univ Post & Telecoms Coll Internet Things Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Coll Comp Sci & Engn Nanjing Peoples R China Nanjing Univ Finance & Econ Jiangsu Prov Key Lab Business Nanjing Peoples R China

ISBN: (纸本)9783642543708;9783642543692

Discovering communities is crucial for studying the structure and dynamics of networks. Groups of related nodes in the community often correspond to functional subunits such as protein complexes or social spheres. The modularity optimization method is typically an effective algorithm with global objective function. In this paper, we attempt to further enhance the quality of modularity optimization by mining local close-knit structures. First, both periphery and core close-knit structures are defined, and several fast mining and merging algorithms are presented. Second, a novel Fast Newman (FN) algorithm named NFN incorporating local structures into global optimization is proposed. Experimental results in terms of both internal and external on six real-world social networks have demonstrated the effectiveness of NFN on community detection.

关键词： Community Detection modularity optimization Fast Newman Algorithm Close-knit Structures

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