Article
Computer Science, Hardware & Architecture
Huichun Li, Chengli Zhao, Yangyang Liu, Xue Zhang
Summary: An anomaly detection method based on network projection is proposed, which identifies the bipartite structure of the network, constructs a projection network using cosine similarity function, and uses one-class SVM algorithm for anomaly detection. Experiments show higher precision in identifying anomalous addresses in large-scale networks.
Article
Computer Science, Artificial Intelligence
Marija Stankova, Stiene Praet, David Martens, Foster Provost
Summary: The paper proposes a three-stage classification framework for handling large bipartite graph datasets effectively. This framework allows exploration of the design space by mixing and matching different functions to create new techniques. Through empirical study, the SW-transformation emerges as one of the best-performing combinations.
Article
Computer Science, Artificial Intelligence
Ming Gao, Xiangnan He, Leihui Chen, Tingting Liu, Jinglin Zhang, Aoying Zhou
Summary: Recent years have seen a surge of interest in network representation learning, with most research focusing on homogeneous or heterogeneous networks. However, there has been relatively little research on NRL for bipartite networks. This work introduces BiNE, a new solution that takes into account the unique properties of bipartite networks, such as the long-tail distribution of vertex degrees and implicit connectivity relations between vertices of the same type.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Mathematics
Daniela Aguirre-Guerrero, Roberto Bernal-Jaquez
Summary: Scientific research often involves collaboration among researchers, and coauthorship networks are a common means of exploring these collaborations. However, traditional coauthorship networks fail to capture the strength of scientific collaborations due to their use of simple links. In this study, we propose a novel multilayer network model that captures coauthorship relations' strength and employs a convergence index to determine the collaboration order. Applying this methodology to Mexican universities, we find that community structure emerges in low-order collaborations and higher-order collaborations increase clustering and centrality measures. Our methodology provides valuable insights into scientific collaborations and can be applied to other domains.
Article
Physics, Multidisciplinary
Emiliano Marchese, Guido Caldarelli, Tiziano Squartini
Summary: This study presents a unified, surprise-based framework for detecting mesoscale network structures. A general approach is provided by considering weighted cases and six variants of surprise. The performances are demonstrated using synthetic benchmarks and real-world configurations, and a Python code implementing all variants of surprise is provided.
COMMUNICATIONS PHYSICS
(2022)
Article
Computer Science, Artificial Intelligence
Huan Qing, Jingli Wang
Summary: This article introduces a method for detecting community structure in weighted bipartite graph data and proposes two models, one of which addresses the distribution restriction in the original method, and the other considers the variation in node degree. The effectiveness of these methods is illustrated through simulated and empirical examples.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Anthropology
Zachary P. Neal, Rachel Domagalski, Xiaoqin Yan
Summary: The research found that Republicans and women in the US House of Representatives tend to collaborate with same-party and same-gender political partners. However, party homophily is stronger than gender homophily, highlighting the significance of party identity over gender identity for legislators.
Article
Ecology
Jinbao Liao, Daniel Bearup, Giovanni Strona
Summary: The structure of interactions between species within a community is crucial for maintaining biodiversity. Previous studies have shown that the effects of these structures differ depending on the type of interaction. However, a new study using a patch-dynamic metacommunity framework found that the qualitative differences between antagonistic and mutualistic systems disappear, and nestedness and modularity interact to promote metacommunity persistence.
Article
Computer Science, Artificial Intelligence
Cangqi Zhou, Jing Zhang, Kaisheng Gao, Qianmu Li, Dianming Hu, Victor S. Sheng
Summary: This paper proposes a novel Symmetric Neighborhood Convolution method for representation learning in bipartite networks. The method utilizes the symmetry property and convolution kernels to extract features from neighborhood for nodes in each class, and is evaluated on real-world datasets through link prediction and recommendation tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Anthropology
Alessandro Spelta, Nicolo Pecora, Paolo Pagnottoni
Summary: In this paper, a massive attack-focused terrorist database is used to analyze the structural evolution of terrorists-targets relationships. Two novel measures are introduced to assess the harmfulness and vulnerability of terrorist and target groups, both locally and globally. Statistical validation shows that these measures provide new information and are useful in evaluating attack preventive strategies.
Article
Economics
Ben Derudder
Summary: This paper critically reviews the urban systems literature through the lens of 'network analysis'. It focuses on the 'world city network' research and discusses the potential and challenges associated with the use of network analysis. The authors emphasize the importance of continuous evaluation of the real potential of network analysis.
TIJDSCHRIFT VOOR ECONOMISCHE EN SOCIALE GEOGRAFIE
(2021)
Article
Mathematics, Interdisciplinary Applications
Lixing Lei, Junzhong Yang
Summary: The study investigates emergent patterns on duplex networks by studying the coupled FitzHugh-Nagumo model, finding that dominant network modes on duplex networks are related to the most unstable network modes in monolayer networks, and developing approximation methods to address the instability of homogeneous equilibrium on duplex networks, which could serve as a tool for analyzing patterns on duplex networks.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Geochemistry & Geophysics
Jia Liu, Wenhua Zhang, Fang Liu, Liang Xiao
Summary: This article proposes a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection, which can adapt to different types of remote sensing images and scenarios.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Fan Niu, Xiaowei Zhao, Jun Guo, Mei Shi, Xiaoxia Liu, Baoying Liu
Summary: Recent unsupervised dimension reduction algorithms use similarity graphs to preserve local structure while reducing dimension but are limited by their time complexity and susceptibility to outliers. To address these issues, we propose FRUDR-ABG, a fast and robust unsupervised projection model that uses a bipartite graph to preserve local geometric structure, reducing running time and improving efficiency. We introduce a criterion based on the l2,1 norm to reduce the negative influence of outliers and a strategy for joint learning of global and local structures. Experimental results demonstrate that FRUDR-ABG outperforms existing methods in terms of efficiency and recognition performance.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xianhang Zhang, Hanchen Wang, Jianke Yu, Chen Chen, Xiaoyang Wang, Wenjie Zhang
Summary: This paper proposes a bipartite graph-based capsule network, called Bipartite Capsule Graph Neural Network (BCGNN), for bipartite graph classification task. BCGNN utilizes the capsule network and obtains information between the same type vertices in the bipartite graphs by constructing the one-mode projection. Extensive experiments on real-world datasets are conducted to demonstrate the effectiveness of the proposed method.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
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)