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local community detection in Multi-Attributed Road-Social Networks

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3514-3527页

作者： Ni, Li Li, Qiuyu Zhang, Yiwen Luo, Wenjian Sheng, Victor S. Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Guangdong Prov KeyLaboratory Novel Secur Intelligence Technol Shenzhen 518055 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Texas Tech Univ Sch Dept Comp Sci Lubbock TX 79409 USA

The information available in multi-attributed road-social networks includes network structure, location information, and numerical attributes. Most studies mainly focus on mining communities by combining structure with attributes or structure with location, which do not consider structure, attributes, and location simultaneously. Therefore, we propose a parameter-free algorithm, called LCDMRS, to mine local communities in multi-attributed road-social networks. LCDMRS extracts a sub-network surrounding the given node and embeds it to generate the vector representations of nodes, which incorporates both structural and attributed information. Based on the vector representations of nodes, the average cosine similarity between nodes is designed to ensure both the structural and attributed cohesiveness of the community, while the community node density is designed to ensure the spatial cohesiveness of the community. Targeting the community node density and cosine similarity of nodes, LCDMRS takes the given node as the starting node and employs the community dominance relation to expand the community outward. Experimental results on multiple real-world datasets demonstrate LCDMRS outperforms comparison algorithms.

关键词： Vectors Roads Nickel Matrix decomposition Data mining Costs Social networking (online) Linear programming Computer science Training Multi-attributed road-social networks multi-attributed community local community detection dominance relation

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local community detection Based on Core Nodes using Deep Feature Fusion

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-21页

作者： Guo, Xingjun Li, Xiaohong Shi, Wanyao Wang, Siwei Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

Unlike global community detection, local community detection is to identify a cluster of nodes sharing similar feature information based on a given seed. The accuracy of many local community detection algorithms heavily relies on the quality of seed nodes. Only high-quality seed nodes can accurately detect local communities. At the same time, the inability to effectively obtain node attributes and structural information also leads to an increase in subgraph clustering error rates. This paper proposes a local community detection based on Core Nodes using deep feature fusion, named LCDCN. We find the nearest nodes for the seed nodes, then construct a k-subgraph through a specific subgraph extractor based on the core nodes. Subsequently, two deep encoders are employed to encode and fuse the attribute and structure information of the subgraph, respectively. Finally, the local community is discovered by optimizing the fused feature representation through a self-supervised optimization function. Extensive experiments on 10 real and 4 synthetic datasets demonstrate that LCDCN outperforms its competitors in performance.

关键词： local community detection Clustering Seed selection Feature fusion

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local community detection in Multiple Private Networks

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第5期18卷 1-21页

作者： Ni, Li Ye, Rui Luo, Wenjian Zhang, Yiwen Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the local Collaborative community detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network's private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

关键词： community detection local community detection local collaborative community detection Multi-network local modularity

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local community detection Algorithm Based on Alternating Strategy of Strong Fusion and Weak Fusion

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 818-831页

作者： Shang, Ronghua Zhang, Weitong Zhang, Jingwen Jiao, Licheng Li, Yangyang Stolkin, Rustam Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

Existing fusion-based local community detection algorithms have achieved good results. However, when assigning a node to a community, similarity functions are sometimes used, which only use node information, while ignoring connection information within the community. These algorithms sometimes fail to find influential nodes, which eventually leads to the failure to find a complete local community. To address these problems, a new local community detection algorithm is proposed in this article. Two strategies, of strong fusion followed by weak fusion, are used alternately to fuse nodes. Compared with using two fusion strategies alone, the alternating loop method can improve the solution of the algorithm in each stage. In strong fusion, we propose a new membership function that considers both node information and connection information in the local community. This improves the quality of the fused node while preserving the structure of the current community. In weak fusion, we propose a parameter-based similarity measure, which can detect influential nodes for a local community. We also propose a local community evaluation metric, which does not require true division to determine the optimal local community under different parameters. Experiments, compared to six state-of-the-art algorithms, show that the proposed algorithm improves accuracy and stability, and also demonstrate the effectiveness of the new local community evaluation metrics in parameter selection.

关键词： detection algorithms Heuristic algorithms Optimization Indexes Complex networks Topology Social networking (online) Evaluation metrics local community detection membership function strong fusion weak fusion

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local community detection algorithm based on local modularity density

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APPLIED INTELLIGENCE 2022年第2期52卷 1238-1253页

作者： Guo, Kun Huang, Xintong Wu, Ling Chen, Yuzhong Fuzhou Univ Coll Math & Comp Sci Fujian Key Lab Network Comp & Intelligent Informa Fuzhou 350116 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350116 Peoples R China

Compared to global community detection, local community detection aims to find communities that contain a given node. Therefore, it can be regarded as a specific and personalized community detection task. local community detection algorithms based on modularity are widely studied and applied because of their concise strategies and prominent effects. However, they also face challenges, such as sensitivity to seed node selection and unstable communities. In this paper, a local community detection algorithm based on local modularity density is proposed. The algorithm divides the formation process of local communities into a core area detection stage and a local community extension stage according to community tightness based on the Jaccard coefficient. In the core area detection stage, the modularity density is used to ensure the quality of the communities. In the local community extension stage, the influence of nodes and the similarity between the nodes and the local community are utilized to determine boundary nodes to reduce the sensitivity to seed node selection. Experimental results on real and artificial networks demonstrated that the proposed algorithm can detect local communities with high accuracy and stability.

关键词： Complex network local community detection local modularity density community extension

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local community detection from Noisy Graph Streams 5

Local Community Detection from Noisy Graph Streams

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Yang, Han Wang, Lingxiang Wang, Qingren Wang, Huabin Zhang, Yiwen Zhong, Hong Yan, Dengcheng School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9798331506612

The demand for real-time graph analysis has encouraged the research of online community detection methods. However, these methods often assume clean data, which is uncommon in reality. How to further improve the performance and robustness of online community detection for real-world networks has garnered the great attention. In this paper, we propose NgsLCD, an algorithm that identifies valid edges in noisy streams to expand communities and mitigate noise effects. It also uses a node-community similarity metric to filter out incorrectly added nodes. Tests on real networks show NgsLCD reduces noise impact and maintains robust detection performance across varying noise rate. © 2024 IEEE.

关键词： local community detection Min-hash Noisy graph Stream

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Evolutionary Multitasking local community detection on Attributed Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1624-1639页

作者： Zhang, Lei Li, Bin Ni, Li Yang, Haipeng Cao, Renzhi Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230039 Peoples R China Pacific Lutheran Univ Sch Comp Sci Tacoma WA 98447 USA

local community detection (LCD) aims to find a community containing a given starting node using local information, which becomes a hot research topic in the area of community detection. Most mainstream studies in this field only consider network topology information, neglecting node attribute information. Some studies consider both, but these methods are limited by inflexible predefined structures. When the local community topology structure is unclear or the node attributes are heterogeneous, the performance of these methods decreases. In this paper, in order to better balance topological and attribute information, we propose an evolutionary multitasking local community detection (EMLCD) framework to solve the problem of local community detection on attributed networks. In EMLCD, the LCD problem is formulated as a multitasking optimization problem with two tasks, that is, Top-Task detects local community with tight topological connections, and Att-Task detects local community with homogeneous node attributes. The advantage of using evolutionary multitasking to detect local community on attributed networks is that the correlation between these tasks can be used to enhance detection performance. A novel knowledge transfer strategy is proposed to facilitate positive transfer of knowledge between the two tasks by leveraging the complementarity of the topology information and attribute information. Finally, a solution selection strategy is proposed to guide the decision maker in selecting the ideal solution from the set of solutions generated by the two tasks. Experimental results show that the proposed framework is very effective in solving the problem of local community detection on attributed networks.

关键词： Task analysis Optimization Multitasking Knowledge transfer Topology Network topology Liquid crystal displays Attributed networks local community detection multi-objective optimization multitasking optimization

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Streaming local community detection Through Approximate Conductance

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IEEE TRANSACTIONS ON BIG DATA 2024年第1期10卷 12-22页

作者： Wang, Meng Yang, Yanhao Bindel, David He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

community is a universal structure in various complex networks, and community detection is a fundamental task for network analysis. With the rapid growth of network scale, networks are massive, changing rapidly, and could naturally be modeled as graph streams. Due to the limited memory and access constraint in graph streams, existing non-streaming community detection methods are no longer applicable. This raises an emerging need for online approaches. In this work, we consider the problem of uncovering the local community containing a few query nodes in graph streams, termed streaming local community detection. This new problem raised recently is more challenging for community detection, and only a few works address this online setting. Correspondingly, we design an online single-pass streaming local community detection approach. Inspired by the local property of communities, our method samples the local structure around the query nodes in graph streams and extracts the target community on the sampled subgraph using our proposed metric called approximate conductance. Comprehensive experiments show that our method remarkably outperforms the streaming baseline on both effectiveness and efficiency, and even achieves similar accuracy compared to the state-of-the-art non-streaming local community detection methods that use static and complete graphs.

关键词： Network analysis graph stream local community detection approximate conductance

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Semi-Supervised local community detection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第2期36卷 823-839页

作者： Ni, Li Ge, Junnan Zhang, Yiwen Luo, Wenjian Sheng, Victor S. Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

Owing to the lack of a universal definition of communities, some semi-supervised community detection approaches learn the concept of community structures from known communities, and then dig out communities using learned concepts of communities. In some cases, users are only interested in the community containing a given node. However, communities detected by these semi-supervised approaches may not contain a given node. Besides, these methods traverse the entire network to detect many communities and cost more resources than a local algorithm. Therefore, it is necessary and meaningful to find the local community that contains a given node with prior information on the local network around the given node. We call this a Semi-supervised local community detection (SLCD) problem. In this paper, prior information refers to certain known communities. To address the SLCD problem, we propose the Semi-supervised local community detection with the Structural Similarity algorithm, called SLSS, which uses some known communities instead of all known communities. The idea of SLSS is to use the structural similarity between the known communities and the detected community, calculated by the graph kernel, to guide the expansion of the community. Experimental results show that SLSS outperforms other algorithms on six real-world datasets.

关键词： community detection local community detection semi-supervised local community detection structural similarity

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A local community detection Method Based on Folded Subgraph

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 3869-3880页

作者： Zhang, Mengting Bi, Weihong Zhejiang University School of Mathematical Sciences Hangzhou310000 China

community structure refers to the "small groups" in the network. Detecting community structure in networks has significant application value. With the continuous expansion and complexity of the network, the global information of the network is often difficult to obtain. On the other hand, in some cases, we pay more attention to the local community where the given node is located. local community detection methods detect local community structure by using local information from a given node. However, many local community detection methods encounter the problem of precision limitation. Therefore, in order to alleviate such problems, we propose the FG-based method in this paper. Based on the characteristics of complex networks, a folded subgraph method is designed to consider some similar nodes as single nodes, reducing the impact of noise in the network. Furthermore, based on the folded subgraph, FG-based method designs a three-stage local expansion strategy, in which nodes with different characteristics are added to the local community in each stage. We conduct experiments on datasets and find that the FG-based method can improve the recall and precision of local community structures. © 1989-2012 IEEE.

关键词： Noise Hands Data Mining Complex Networks Training Topology Social Networking Online Proteins Network Analyzers Measurement local community detection Precision Limitation local Modularity Social Network local Communities local Method community detection community detection Methods local community detection community Structure local Information Precision And Recall Global Information Limited Precision local Expansion Expansion Strategy local community Structure Lack Of Information Hyperparameters Performance Of Method Less Than Or Equal Limited Resolution local Optimum Neighboring Nodes Link Density Nodes In community Performance Of detection Methods Improvement In Recall Real World Networks Traditional Communities Relaxation Strategies Traditional detection Greedy Strategy Different Types Of Nodes

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