Review
Computer Science, Information Systems
Norah Alotaibi, Delel Rhouma
Summary: This article provides an overview of the characteristics and challenges of community detection in dynamic social networks, and compares state-of-the-art methods. Researchers can use this survey to find the best methods and choose relevant future directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongtao Liu, Jiahao Wei, Tianyi Xu
Summary: In this paper, a new community detection method called CPGC is proposed, which combines the community perspective and graph convolution network to address the challenges of overlapping communities in attributed networks. CPGC achieves state-of-the-art results in nonoverlapping or overlapping communities, as demonstrated by experiments on various real-world networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sneha Mishra, Shashank Sheshar Singh, Shivansh Mishra, Bhaskar Biswas
Summary: A tree-based algorithm for dynamic community detection in social networks is proposed, tackling challenges in dynamic networks and demonstrating superior performance over state-of-the-art algorithms in experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Hyungho Byun, Younhyuk Choi, Chong-Kwon Kim
Summary: This paper proposes a novel scheme called ANES for representation learning and social link inference based on user trajectory data. It extracts behavioral patterns from both trajectory data and the structure of User-POI bipartite graphs, outperforming state-of-the-art baselines in extensive experiments on real-world datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Marco De Luca, Anna Rita Fasolino, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperli, Porfirio Tramontana
Summary: In this paper, a novel heterogeneous graph-based model is proposed to capture and handle the complex and strongly-correlated information of a software Developer Social Network (DSN) for analytic tasks. The problem of automatically discovering communities of software developers sharing interests for similar projects is addressed using Social Network Analysis (SNA) findings, and graph embedding techniques are utilized to overcome the large graph size. The proposed approach is evaluated against state-of-the-art approaches in terms of efficiency and effectiveness using the GitHub dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Shunjie Yuan, Hefeng Zeng, Ziyang Zuo, Chao Wang
Summary: This study proposes an overlapping community detection method CDMG based on Graph Convolutional Networks (GCN) to maximize the Markov stability of community structure. Extensive experiments demonstrate the superiority of CDMG compared to other established community detection algorithms. The performance of CDMG can be further improved by utilizing the optimal Markov time, which is found using a trichotomy-based method based on the influence of Markov time on CDMG performance.
COMPUTER COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Fengnan Gao, Zongming Ma, Hongsong Yuan
Summary: This paper presents a simple community detection algorithm that achieves consistency and optimality for a broad and flexible class of sparse latent space models. The algorithm is based on spectral clustering and local refinement through normalized edge counting, and it is easy to implement and computationally efficient. The proof of optimality is based on an interesting equivalence between likelihood ratio test and edge counting in a simple vs. simple hypothesis testing problem, which could be of independent interest.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Information Systems
Arnab Dey, B. Rushi Kumar, Bishakha Das, Arnab Kumar Ghoshal
Summary: Social networks have become essential in today's world and are increasingly used for communication worldwide. The large amount of data transferred over social networks necessitates the need for security precautions. This paper introduces a new technique that detects anomalies in a network from a global perspective using the network community structure, outperforming existing algorithms in terms of accuracy and speed.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiaoyan Bi, Tie Qiu, Wenyu Qu, Laiping Zhao, Xiaobo Zhou, Dapeng Oliver Wu
Summary: This article proposes a routing method based on dynamic transient social communities (DTSC) to optimize routing and forwarding performance in mobile social networks (MSNs). By calculating the similarity of contact nodes and conducting community detection, the measurement value and routing algorithm for message delivery capability are designed to ensure successful message delivery in a short time.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Zhigang Liu, Xin Luo, Zidong Wang, Xiaohui Liu
Summary: This study proposes a Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model for community detection. Experimental results demonstrate that the proposed model significantly outperforms benchmarks and state-of-the-art models in achieving highly-accurate community detection results.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Vincenzo Moscato, Giancarlo Sperli
Summary: Detecting users' communities in Online Social Networks is crucial for enhancing the effectiveness of diffusion of new ideas, improving recommendation suggestions, and finding experts. Different community detection techniques based on game theory, artificial intelligence, and fuzzy strategies are compared for various OSN models, highlighting pros and cons. Challenges and open issues in the community detection problem are discussed for future research.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Anwar Said, Muhammad Umar Janjua, Saeed-Ul Hassan, Zeeshan Muzammal, Tania Saleem, Tipajin Thaipisutikul, Suppawong Tuarob, Raheel Nawaz
Summary: This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework, investigating various valuable aspects of the Ethereum network. The study explores the change in wealth distribution and accumulation on the Ethereum Featured Transactional Network (EFTN) and employs state-of-the-art Variational Graph Auto-Encoders for link predictability, demonstrating outstanding prediction accuracy on Ethereum networks. Visualized and summarized statistic usages of the Ethereum network in experiments allow for conjectures on current use and future development of this technology.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Di Jin, Xiaobao Wang, Dongxiao He, Jianwu Dang, Weixiong Zhang
Summary: The study discusses the issues of community detection in real networks and proposes a new Bayesian probabilistic approach to address these challenges. By exploring the correlation between communities and topics, the new method aims to discover link communities and extract semantically meaningful community summaries simultaneously. Experimental results demonstrate the effectiveness of the new approach and its ability to provide rich explanations through multiple topical summaries per community if desired.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
Summary: This article discusses the applications of deep learning in community detection, providing a classification of different methods and models. It introduces popular datasets, evaluation metrics, and open-source implementations, and discusses the practical applications of community detection in various domains. The article concludes with suggestions for future research directions in this growing field of deep learning.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Aniello De Santo, Antonio Galli, Vincenzo Moscato, Giancarlo Sperli
Summary: This paper introduces an innovative approach for semi-supervised community detection using Convolutional Neural Networks, optimizing computational cost and outperforming existing techniques on large datasets in terms of running time and F1 scores.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics, Applied
Mengjie Lv, Shuming Zhou, Jiafei Liu, Xueli Sun, Guanqin Lian
DISCRETE APPLIED MATHEMATICS
(2019)
Article
Physics, Applied
Xuequn Li, Shuming Zhou, Jiafei Liu, Gaolin Chen, Zhendong Gu, Yihong Wang
INTERNATIONAL JOURNAL OF MODERN PHYSICS B
(2019)
Article
Computer Science, Theory & Methods
Jiafei Liu, Shuming Zhou, Zhendong Gu, Qianru Zhou, Dajin Wang
Summary: The fault diagnosability of a network is a crucial indicator of its reliability, with the original definition often being too strict. To better measure actual reliability, many variants of diagnosability have been proposed.
THEORETICAL COMPUTER SCIENCE
(2021)
Article
Mathematics, Applied
Liqiong Xu, Shuming Zhou, Jiafei Liu, Shanshan Yin
Summary: This paper discusses the reliability measure of multiprocessor systems based on edge connectivity, examining the impacts of extra edge connectivity and component edge connectivity on the robustness of multiprocessor systems. It also explores various types of connectivity in enhanced hypercubes and folded hypercubes, providing a detailed analysis and extension of previous results.
DISCRETE APPLIED MATHEMATICS
(2021)
Article
Physics, Multidisciplinary
Gaolin Chen, Shuming Zhou, Jiafei Liu, Min Li, Qianru Zhou
Article
Computer Science, Theory & Methods
Qianru Zhou, Shuming Zhou, Jiafei Liu, Xiaoqing Liu
Summary: The paper mainly investigates H-structure-connectivity and H-substructure-connectivity for H being an element of {K-1, K-1, K-1, K-1, K-m (2 <= m <= d + 1), C-4}, respectively.
THEORETICAL COMPUTER SCIENCE
(2021)
Article
Mathematics, Applied
Hong Zhang, Shuming Zhou, Jiafei Liu, Qianru Zhou, Zhengqin Yu
Summary: Connectivity and diagnosability are crucial metrics for the reliability of multiprocessor systems. This paper investigates these parameters for a compound graph DQcube(DQ(n)), based on disc-ring and hypercube, and presents several key findings.
DISCRETE APPLIED MATHEMATICS
(2021)
Article
Computer Science, Theory & Methods
Jiafei Liu, Shuming Zhou, Eddie Cheng, Qianru Zhou, Xiaoqing Liu
Summary: Attackers tend to target vulnerable networks due to lower cost and higher probability of success. Designers focus on the robustness and reliability of massively networked systems, with connectivity and diagnosability being important indicators. The n-dimensional cactus-based network is introduced, with characterization of its properties and connectivity metrics.
THEORETICAL COMPUTER SCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Jiafei Liu, Shuming Zhou, Eddie Cheng, Gaolin Chen, Min Li
Summary: This paper focuses on the reliability evaluation and diagnostic capability of a novel multiprocessor system, and proposes a primary strategy for identifying faulty processors in the system.
PARALLEL PROCESSING LETTERS
(2021)
Article
Mathematics, Applied
Jiafei Liu, Shuming Zhou, Hong Zhang, Gaolin Chen
Summary: With the increasing popularity of large scale networks in various fields, the robustness analysis of networks against faulty processors has become a topic of interest. This paper investigates the size and structure of the surviving network when certain faulty vertices are removed in the burnt pancake network.
DISCRETE APPLIED MATHEMATICS
(2022)
Article
Computer Science, Theory & Methods
Jiafei Liu, Shuming Zhou, Dajin Wang, Hong Zhang
Summary: Enhancing the invulnerability of multiprocessor systems against malicious attacks is an important issue in network science and big data era. Component connectivity is a significant metric in evaluating the robustness and fault tolerability of interconnection networks. In this paper, the authors propose some characterizations of the component connectivity of a class of regular networks and establish the relationship between component connectivity and component diagnosability. Furthermore, the (h+1)-component diagnosability of compound networks based on hypercube is presented.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Jiafei Liu, Qianru Zhou, Zhengqin Yu, Shuming Zhou
Summary: This study determines the diagnosability of triangle-free regular networks under the hybrid PMC model and applies the results to various regular networks, such as hypercube networks and hypercube-based compound networks.
COMPUTING AND COMBINATORICS (COCOON 2021)
(2021)
Article
Computer Science, Interdisciplinary Applications
Jiafei Liu, Shuming Zhou, Zhendong Gu, Yihong Wang, Qianru Zhou
PARALLEL PROCESSING LETTERS
(2019)
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
