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学者姓名：吴伶
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Although incremental methods are widely used in community detection, their error accumulation problem remains unresolved. Additionally, current methods typically identify events only after community detection has been completed for all time snapshots, lacking consideration of the impact of events on community structure during evolution. Therefore, this paper proposes a framework called Tracking dynamic community evolution based on Social Relevance and Strong Events(TranSiEnt). For the first time, TranSiEnt integrates evolution event identification with dynamic community updating, classifying evolution events into ordinary events and Strong Events based on the influence of the relevant communities. During dynamic community updating, TranSiEnt employs a path diffusion strategy to determine core nodes for community detection, establishing the initial community structure. Using an incremental approach, the framework expands the influence range of incremental nodes in communities experiencing Strong Events. It again conducts precise community detection on all affected nodes to reduce error accumulation, ultimately optimizing community partitioning. TranSiEnt was subjected to objective accuracy experiments on real and synthetic datasets, using modularity, NMI, and EMA as performance evaluation metrics. T-tests were used to verify the significance of the performance improvement of the TranSiEnt algorithm. The experimental results show that TranSiEnt performs better in dynamic community detection and evolution event tracking, significantly improving over existing methods.
Keyword ：
Dynamic community detection Dynamic community detection Social Relevance Social Relevance Strong Event Strong Event
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| GB/T 7714 | Wu, Ling , Xie, Xiaohua , Chen, Chengkai et al. Tracking dynamic community evolution based on Social Relevance and Strong Events [J]. | KNOWLEDGE AND INFORMATION SYSTEMS , 2025 . |
| MLA | Wu, Ling et al. "Tracking dynamic community evolution based on Social Relevance and Strong Events" . | KNOWLEDGE AND INFORMATION SYSTEMS (2025) . |
| APA | Wu, Ling , Xie, Xiaohua , Chen, Chengkai , Yang, Yingjie , Guo, Kun . Tracking dynamic community evolution based on Social Relevance and Strong Events . | KNOWLEDGE AND INFORMATION SYSTEMS , 2025 . |
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A clear game process helps to track community generation and evolution, so non-cooperative and cooperative games are applied to community detection. However, non-cooperative games focus on the competition between nodes, disregarding their cooperation. Solely considering individual perspectives often results in insufficient precision. Cooperative games consider the interests of both coalitions and individual. Nevertheless, involving a large number of participants in cooperative games can lead to high computational complexity and slow convergence. In this study, a fast community detection model called FCDG is proposed. It combines non-cooperative and cooperative games by exploring candidate communities and optimizing community merging. Firstly, a intimate core group identification strategy based on node mutual intimacy is designed to accelerate the convergence of candidate community detection using non-cooperative games and maximize individual benefits. Secondly, building upon of candidate community, a candidate community merging approach based on cooperative games is devised to achieve community optimal solution. The performance of FCDG is evaluated on both real-world and synthetic datasets. Experimental results demonstrate that FCDG effectively discovers community structure with higher accuracy and robustness compared to other baseline algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Complex networks Complex networks Game theory Game theory Lead compounds Lead compounds Merging Merging Population dynamics Population dynamics
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| GB/T 7714 | Wu, Ling , Yuan, Mao , Guo, Kun . Fast Community Detection Based on Integration of Non-cooperative and Cooperative Game [C] . 2024 : 276-286 . |
| MLA | Wu, Ling et al. "Fast Community Detection Based on Integration of Non-cooperative and Cooperative Game" . (2024) : 276-286 . |
| APA | Wu, Ling , Yuan, Mao , Guo, Kun . Fast Community Detection Based on Integration of Non-cooperative and Cooperative Game . (2024) : 276-286 . |
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In order to address the problem of reconstruction and retraining time overhead in representation learning processing dynamic networks, this paper proposes an incremental inductive dynamic network community detection algorithm (IINDCD). First, the algorithm uses an attention mechanism to capture node neighborhood information and learn node representations by neighborhood aggregation induction while enhancing low-order structural representations. Second, the design uses random walking to capture high-order information and use it to construct node initial features for input into the attentional autoencoder, which effectively fuses high- and low-order structural features. Finally, the algorithm introduces the ideas of incremental update and model reuse for dynamic representation learning, constructs incremental node sets for updating the model, reduces training overhead, and quickly obtains node representation vectors for new moments of the network, then completing dynamic network community detection. IINDCD runs without reconstruction and with low retraining overhead. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Learning algorithms Learning algorithms Learning systems Learning systems Population dynamics Population dynamics
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| GB/T 7714 | Wu, Ling , Zhuang, Jiangming , Guo, Kun . Incremental Inductive Dynamic Network Community Detection [C] . 2024 : 93-107 . |
| MLA | Wu, Ling et al. "Incremental Inductive Dynamic Network Community Detection" . (2024) : 93-107 . |
| APA | Wu, Ling , Zhuang, Jiangming , Guo, Kun . Incremental Inductive Dynamic Network Community Detection . (2024) : 93-107 . |
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Community detection on attributed networks is a method to discover community structures within attributed networks. By applying community detection on attribute networks, we can better understand the relationships between nodes in real-world networks. However, current algorithms for community detection on attribute networks rely on hyper-parameters, and it is difficult to obtain an ideal result when facing networks with inconsistent attributes and topology. Consequently, we propose an Unsupervised Multi-population Evolutionary Algorithm (UMEA) for community detection in attributed networks. This algorithm adds edges between nodes based on attribute similarity, allowing it to combine attribute information during the process of community detection. In addition, this algorithm determines the optimal number of added edges autonomously through communication and learning between multiple populations. Furthermore, we propose a series of strategies to accelerate population convergence for the locus-based encoding. Experiments have demonstrated that our algorithm outperforms the benchmark algorithms in both real and artificial networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Data mining Data mining Evolutionary algorithms Evolutionary algorithms Population dynamics Population dynamics
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| GB/T 7714 | Wu, Junjie , Wu, Lin , Guo, Kun . Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks [C] . 2024 : 152-166 . |
| MLA | Wu, Junjie et al. "Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks" . (2024) : 152-166 . |
| APA | Wu, Junjie , Wu, Lin , Guo, Kun . Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks . (2024) : 152-166 . |
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Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Complex networks Complex networks Heterogeneous networks Heterogeneous networks Population dynamics Population dynamics
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| GB/T 7714 | Liu, Junjie , Guo, Kun , Wu, Ling . ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks [C] . 2024 : 182-194 . |
| MLA | Liu, Junjie et al. "ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks" . (2024) : 182-194 . |
| APA | Liu, Junjie , Guo, Kun , Wu, Ling . ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks . (2024) : 182-194 . |
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Subspace clustering, known for its effectiveness in handling high-dimensional data, has attracted attention. And the autoencoder can discover hidden features within a large dataset, yet it faces challenges in utilizing attribute information for reconstruction and capturing complex spatial structural information. To tackle these issues, we propose a community detection algorithm named Deep Attention Autoencoder Based on Subspace Constraints (DAASC). First, we design an attribute topology fusion strategy to integrate attribute information into the reconstruction of the decoder. Then, we design a subspace autoencoder strategy, using the concept of subspaces to construct the loss function, to capture the spatial structural information of the data. Experiments conducted on both real-world and synthetic networks to compare DAASC with several state-of-the-art community detection algorithms demonstrate its exceptional accuracy and robustness. © 2024 IEEE.
Keyword ：
Clustering algorithms Clustering algorithms Complex networks Complex networks Deep learning Deep learning Large datasets Large datasets Learning systems Learning systems Population dynamics Population dynamics Signal detection Signal detection
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| GB/T 7714 | Cai, Ziqi , Chen, Jianguo , Wu, Ling . Community Detection with Deep Attention Autoencoder Based on Subspace Constraints [C] . 2024 : 95-98 . |
| MLA | Cai, Ziqi et al. "Community Detection with Deep Attention Autoencoder Based on Subspace Constraints" . (2024) : 95-98 . |
| APA | Cai, Ziqi , Chen, Jianguo , Wu, Ling . Community Detection with Deep Attention Autoencoder Based on Subspace Constraints . (2024) : 95-98 . |
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This paper presents a vehicle route planning method based on game theory principles and innovative utility functions. By addressing the complexities of real-Time traffic congestion, the proposed framework offers a dynamic allocation strategy for rational decision-making. The utility function, which inte-grates traffic flow volume, road capacity, and congestion effects, provides accurate travel time estimations. Mathematical analysis and validation, including genetic algorithms, underscore the framework's robustness. Equilibrium solutions reveal allocation strategies responsive to varying road conditions. Comparative scenarios demonstrate the utility function's effectiveness in guiding enterprises' decisions. This research extends beyond static models, envisioning a future of data integration, multi-objective optimization, adaptive learning, and eco-friendly navigation. By converging these routes, the paper sets the stage for a smarter, more sustainable transportation landscape. © 2024 IEEE.
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| GB/T 7714 | Liu, Zheng-Tan , Wu, Ling , Chen, Chi-Hua . Game Theory-Based Fastest Route Plan Method for Transportation Network Applications [C] . 2024 . |
| MLA | Liu, Zheng-Tan et al. "Game Theory-Based Fastest Route Plan Method for Transportation Network Applications" . (2024) . |
| APA | Liu, Zheng-Tan , Wu, Ling , Chen, Chi-Hua . Game Theory-Based Fastest Route Plan Method for Transportation Network Applications . (2024) . |
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Abstract ：
云计算与物联网安全课程是信息安全专业本科生的必修课,培养学生运用所学的云计算与物联网技术分析和解决问题.本教学创新成果报告围绕3个课堂教学真实问题:一是学生多学科交叉基础知识不足;二是学生解决实际问题和实践能力不足;三是存在产学落差,学生所学技术无法符合产业需求.并且分别提出3个教学方案解决对应的课堂教学真实问题:一是开发"AI助教"APP,以增强现实(AR)和人工智能(AI)语音问答协助学生的学习过程,结合创新性;二是引入心率带、脑波仪、机器人等设备,强化学生的自主学习动机和学习习惯,培养学生解决问题的思维能力,提升高阶性;三是结合"码云"分享开源代码,由企业下载和评价,增加挑战度.
Keyword ：
人工智能 人工智能 信息教育 信息教育 物联网 物联网
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| GB/T 7714 | 吴伶 , 李小燕 , 陈志华 et al. 人工智能与增强现实应用于本科教育 [J]. | 科学咨询 , 2024 , (4) : 131-134 . |
| MLA | 吴伶 et al. "人工智能与增强现实应用于本科教育" . | 科学咨询 4 (2024) : 131-134 . |
| APA | 吴伶 , 李小燕 , 陈志华 , 钟展良 . 人工智能与增强现实应用于本科教育 . | 科学咨询 , 2024 , (4) , 131-134 . |
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Community detection is an important research direction in complex network analysis that can help us discover valuable network structures. The community detection algorithms based on multiobjective particle swarm optimization encode community membership of nodes in particles and employ evolutionary strategies to search for the optimal community division. Existing algorithms face two challenges: (1) they are inapplicable to large networks because the evolution process is time-consuming; (2) they are easy to fall into local optima. In this paper, we propose a novel algorithm that combines a label-propagation-based multiobjective particle swarm optimization algorithm with a graph attention variational autoencoder to realize community detection. On the one hand, the label propagation strategy is involved in the update of a swarm's particles to speed up its evolution. The optimal solutions found by the particle swarm optimization algorithm are embedded into the objective of the autoencoder to improve the embedding vectors' quality. On the other hand, the embedding vectors are used to improve the solutions of the particle swarm optimization algorithm to avoid its early convergence. The experiments on artificial and real-world networks demonstrate the feasibility and effectiveness of our algorithm compared with some state-of-the-art algorithms.
Keyword ：
Big Data Big Data Community detection Community detection Convergence Convergence Detection algorithms Detection algorithms graph attention variational autoencoder graph attention variational autoencoder label propagation label propagation multiobjective particle swarm optimization multiobjective particle swarm optimization Optimization Optimization Particle swarm optimization Particle swarm optimization Prediction algorithms Prediction algorithms Search problems Search problems
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| GB/T 7714 | Guo, Kun , Chen, Zhanhong , Lin, Xu et al. Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder [J]. | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (2) : 569-583 . |
| MLA | Guo, Kun et al. "Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder" . | IEEE TRANSACTIONS ON BIG DATA 9 . 2 (2023) : 569-583 . |
| APA | Guo, Kun , Chen, Zhanhong , Lin, Xu , Wu, Ling , Zhan, Zhi-Hui , Chen, Yuzhong et al. Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational Autoencoder . | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (2) , 569-583 . |
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The representation learning approach aims to obtain a low-dimensional representation of nodes and accomplish community detection by clustering. Adjacency matrix is the most common form of network representation, but it only represents the direct connection relationship of network nodes and lacks more useful topological information. Existing approaches, such as jaccard coefficient for topology extraction, are still limited to neighborhoods, and the available information is not rich enough. In addition, roles, another vital idea, lack a more profound application to network topology. This paper proposes a novel community detection algorithm based on enhancing graph autoencoder with node structural role (CDESR). On the one hand, the structural role we designed effectively specifies the importance of nodes in the network. Based on this idea, a new strategy for computing node topological relations is proposed for their information extraction. On the other hand, the enhancement matrix constructed using the extracted rich information efficiently optimizes the graph autoencoder to obtain a high-quality representation. The experimental results on real-world and synthetic networks verify the effectiveness of our algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
community detection community detection graph autoencoder graph autoencoder representing learning representing learning structural role structural role
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| GB/T 7714 | Wu, L. , Yang, J. , Guo, K. . Community Detection Based on Enhancing Graph Autoencoder with Node Structural Role [未知]. |
| MLA | Wu, L. et al. "Community Detection Based on Enhancing Graph Autoencoder with Node Structural Role" [未知]. |
| APA | Wu, L. , Yang, J. , Guo, K. . Community Detection Based on Enhancing Graph Autoencoder with Node Structural Role [未知]. |
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