Article
Computer Science, Artificial Intelligence
Di Zhuang, J. Morris Chang, Mingchen Li
Summary: Community detection is crucial for online social network analysis, with most studies focusing on static networks. The proposed DynaMo algorithm aims to detect communities in dynamic networks more effectively and efficiently compared to static algorithms. Extensive experiments show that DynaMo outperforms other dynamic algorithms and is 2 to 5 times faster than the Louvain algorithm on average.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Furkan Oztemiz, Ali Karci
Summary: This study proposes a modularity optimization algorithm to increase clustering success in any network without being dependent on any community detection algorithm. The algorithm transfers nodes at the community boundary to neighboring communities to improve the modularity value. Experimental results show that the proposed method significantly enhances the modularity values of community detection algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Kai Qi, Heng Zhang, Yang Zhou, Yifan Liu, Qingxiang Li
Summary: This study introduces an algorithm called PR-LFM, which combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources. The experimental data demonstrate good results in the resource division of cyberspace.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yiran Chen, Qinma Kang, Wenqiang Duan, Yunfan Shan, Ran Xiao, Yunfan Kang
Summary: This paper proposes a simple and effective iterated local search algorithm coupled with a powerful local search mechanism to solve the community detection problem in large signed networks. By adopting the modularity density criterion, the proposed algorithm demonstrates high-quality solutions compared to state-of-the-art algorithms.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2022)
Article
Computer Science, Artificial Intelligence
Ali Mousavi, Richard G. Baraniuk
Summary: This article introduces a method called the uniform information coefficient (UIC), which is able to infer relationships among variables from large datasets. Compared to traditional methods, the UIC calculation is more efficient and robust to the type of association between variables.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Volkan Tunali
Summary: The study introduces a novel large-scale community detection algorithm based on two new similarity indices to identify and extract community structures in networks. Experimental results demonstrate that the algorithm performs well in networks with increasing size and complexity, outperforming most existing community detection methods in terms of detection performance and computation time.
Article
Statistics & Probability
Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina
Summary: The problem of community detection in networks is usually formulated as finding a single partition of the network into correct number of communities. However, constructing a hierarchical tree of communities is more interpretable and accurate in some cases. A top-down recursive partitioning algorithm can be used to separate nodes into communities by spectral clustering repeatedly, until no further communities are suggested by a stopping rule. This model-free and computationally efficient algorithm outperforms K-way spectral clustering in certain regimes.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Computer Science, Information Systems
Yanan Sun, Zejun Sun, Xinfeng Chang, Zhongqiang Pan, Limin Luo
Summary: Community structure plays an important role in network analysis, but efficient community detection is challenging due to the complexity and dynamic nature of networks. In this paper, a new dynamic model and algorithm based on the fish school effect are proposed for more intuitive community detection. Experimental results show that the proposed algorithm outperforms traditional methods in terms of community detection quality.
Article
Engineering, Multidisciplinary
Junyou Zhu, Chunyu Wang, Chao Gao, Fan Zhang, Zhen Wang, Xuelong Li
Summary: Understanding and discovering community structures of networks are significant in exploring network behaviors and functions. This paper proposes a structural equivalence embedding method (SENMF) based on non-negative matrix factorization to embed node and structural similarities into a low-dimensional vector space for community detection, which shows effectiveness compared to other network embedding and traditional community detection methods.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Physics, Applied
Xinyue Zhou
Summary: This study introduces a fuzzy community detection algorithm based on pointer and adjacency list, which has been verified for correctness and suitability in experiments. The algorithm can store community partition structure and membership values, showing good performance for large-scale network applications.
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2021)
Article
Computer Science, Artificial Intelligence
Wenjian Luo, Daofu Zhang, Li Ni, Nannan Lu
Summary: In this paper, a new method for multiscale local community detection is proposed, which can discover local communities of different scales by introducing a new local modularity. Experimental results on multiple datasets indicate that the detected communities are meaningful and their scale can be reasonably adjusted.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Antonio G. Spampinato, Rocco A. Scollo, Vincenzo Cutello, Mario Pavone
Summary: Community detection, an important research topic in Complex Network Analysis, plays a significant role in interpreting and understanding various systems in neuroscience, biology, social science, and economy. This paper introduces an immune optimization algorithm (opt-IA) for detecting community structures, aiming to maximize the modularity of the identified communities. Compared with 20 heuristics and metaheuristics, opt-IA demonstrates superior performance while being comparable to the Hyper-Heuristic method. The results confirm that opt-IA, despite relying on a purely random process, is reliable and efficient.
Article
Computer Science, Information Systems
Guiqiong Xu, Jiawen Guo, Pingle Yang
Summary: The study proposes an improved label propagation algorithm for community detection by introducing a new similarity measure and an optimization strategy to select initial community centers that are important and far apart from each other, aiming to enhance community quality and detection stability.
Article
Computer Science, Artificial Intelligence
Zhen Wang, Chunyu Wang, Xianghua Li, Chao Gao, Xuelong Li, Junyou Zhu
Summary: Community structure division is a crucial issue in network data analysis, and algorithms based on Markov chains offer promising solutions for community detection. The MCL algorithm utilizes a dynamic process of updating flow distribution matrix and transition matrix, affecting accuracy and computational cost. A Physarum-inspired relationship among vertices is proposed to enhance transition probability in MCL-based community detection algorithms, showing better computational efficiency and detection performance in experiments.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Automation & Control Systems
Yansen Su, Chunlong Liu, Yunyun Niu, Fan Cheng, Xingyi Zhang
Summary: The research introduces a community structure enhancement method, CSE, which enhances the community structure of a network by adding links between nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby making the ambiguous community structure clearer. The experimental results demonstrate the superior performance of CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Huijia Li, Wenzhe Xu, Chenyang Qiu, Jian Pei
Summary: This paper proposes a new Markov clustering algorithm based on the limit state of the belief dynamics model, which aims to accurately and efficiently detect graph cluster configurations. The algorithm guarantees ideal cluster configuration and has a fast convergence speed, as demonstrated in experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Physics, Multidisciplinary
Juan Wang, Chengyi Xia
Summary: This survey reviews recent advances in the field of indirect reciprocity and reputation mechanism through theoretical modeling and behavior experiments. Game models with reputation evaluation show that introducing reputation drastically enhances collective cooperation. Human experiments validate that providing enough information on strategy or reputation enhances cooperative behavior, but link rewiring may dominate human cooperation. Directions for further exploration in real-world populations are identified.
Article
Mathematics, Interdisciplinary Applications
Liangliang Chang, Zhipeng Zhang, Chengyi Xia
Summary: This study explores the effect of delays in the control channel on network evolutionary games, and investigates the strategy synthesis problem using semi-tensor products of matrices. The dynamics of network evolutionary games with control transmission delays are converted into an algebraic expression. Sufficient and necessary conditions for the existence of strategy convergence are derived, and an improved algorithm is developed to design the feedback control law.
DYNAMIC GAMES AND APPLICATIONS
(2023)
Editorial Material
Biology
Chengyi Xia, Zhen Wang
PHYSICS OF LIFE REVIEWS
(2023)
Letter
Computer Science, Information Systems
Wenkai Xu, Li Wang, Shiwen Sun, Chengyi Xia, Zengqiang Chen
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Jiexin Wu, Cunlai Pu, Shuxin Ding, Guo Cao, Chengyi Xia, Panos M. M. Pardalos
Summary: In this paper, a multi-objective optimization problem is formulated to address the issues of traffic congestion and transport delay in complex networks. The proposed solution involves maximizing transport capacity and minimizing the average number of hops. A multi-objective evolutionary algorithm called network centrality guided multi-objective particle swarm optimization (NC-MOPSO) is introduced to solve this problem. Simulation experiments demonstrate that the algorithm outperforms five state-of-the-art alternatives on various metrics.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Mathematics, Applied
Wenbo Li, Yuying Zhu, Chengyi Xia
Summary: The network typology significantly influences the evolutionary dynamics of collective behaviors. By extending the sender-receiver game to multiple communities, we explore the evolution of honest behaviors and identify conditions for promoting their evolution.
Article
Mathematics, Interdisciplinary Applications
Shiqiang Guo, Juan Wang, Dawei Zhao, Chengyi Xia
Summary: This paper improves the multi-player snowdrift game based on scale-free simplicial complexes and introduces four representative second-order assessment norms to analyze the impact of higher-order topology and reputation evaluation on collective cooperation behaviors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Lijin Liu, Meiling Feng, Chengyi Xia, Dawei Zhao, Matjaz Perc
Summary: The interplay between awareness diffusion and epidemic spreading is investigated. The study introduces a two-layer network model to analyze the interaction and takes into account individual heterogeneity in both awareness diffusion and epidemic spreading. The results highlight the complex relationship between awareness diffusion and epidemic spreading.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Yuying Zhu, Chengyi Xia
Summary: This paper investigates the understanding and modeling of spatial collective decision-making behaviors in the evolutionary game context. By developing two types of payoff incentive mechanisms, namely reward and punishment, the study explores the asynchronous best-response dynamics of anti-coordinating agents. The results suggest that both reward and punishment can induce strategy switches, causing a cascading effect in the evolutionary dynamics of the spatial gaming systems. Additionally, the paper calculates the optimal solutions for added incentives under different budget cases, facilitating predictions on how the system dynamics are influenced by the proposed incentive mechanisms.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Meiling Feng, Xuezhu Li, Dawei Zhao, Chengyi Xia
Summary: This study investigates the impact of network topology on trust games and finds that trust can be spread in different network topologies, and cooperation or trust can be maintained at a higher level even in more intense dilemmas. The study also reveals that higher average degree distribution in a network leads to higher trust levels, while higher clustering coefficients result in lower trust levels.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Automation & Control Systems
Jiaxing Chen, Meiling Feng, Dawei Zhao, Chengyi Xia, Zhen Wang
Summary: Epidemiological models based on traditional networks have made important contributions to the analysis and control of malware, disease, and rumor propagation. However, higher-order networks are becoming more effective for modeling epidemic spread and characterizing group interactions. In this article, a composite effective degree Markov chain approach (CEDMA) is proposed to represent epidemic dynamics on higher-order networks. CEDMA accurately captures discontinuous phase transitions and bistability phenomena caused by higher-order interactions and can also predict critical points and phase transitions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics, Applied
Weiwei Han, Zhipeng Zhang, Chengyi Xia
Summary: In this paper, the controllability of networked finite state machine (NFSM) under random channel packet losses is investigated using the matrix semi-tensor product (STP) framework. The NFSM with random channel packet losses is represented as a probabilistic logic representation using a stochastic variable, assumed to follow the Bernoulli binary distribution. The concise validation conditions for the controllability of NFSM with a probability of one are derived through the delicate operation of matrix STP. The validity of the proposed method is demonstrated through a typical computing instance, and the conclusions are beneficial for studying security issues in the system.
MATHEMATICAL MODELLING AND CONTROL
(2023)
Article
Physics, Multidisciplinary
Juan Wang, Shiqiang Guo, Chengyi Xia, Matjaz Perc
Summary: Through experiments and simulations, it has been found that increasing the utility coupling between network layers can enhance the level of cooperation, while increasing the number of 2-simplex interactions tends to decrease cooperation. However, despite this result, the overall level of cooperation on interdependent networks is still higher than that on isolated networks.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2023)
Article
Engineering, Multidisciplinary
Jiaxing Chen, Shiwen Sun, Chengyi Xia, Dinghua Shi, Guanrong Chen
Summary: This article proposes a hypergraph-based model to describe the malware propagation in large-scale wireless networks. The model reveals the mechanism and influencing factors of malware epidemic on wireless networks, and verifies that the heterogeneous distribution of devices on wireless networks can easily lead to malware outbreaks, which are more sensitive to the number of initially infected devices than the propagation on the Internet.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
