Key node identification algorithm for complex network based on improved grey wolf optimization by Qiuyang GU, Bao WU, Zhaoyang SUN, Renyong CHI

“…In recent years, how to select the most influential key node for identification has become the most cutting-edge hot direction in network science.Formulating the problem of maximizing the influence of complex network nodes as an optimization problem whose cost function was expressed as the influence of nodes and the distance between them, measures user influence using Shannon entropy, and solved this problem using an improved gray wolf optimization algorithm.Finally, numerical examples were performed with real complex network datasets.The experimental results show that the proposed algorithm is more accurate and computationally efficient than the existing method.…”
Get full text

Synchronizability of Multilayer Star and Star-Ring Networks by Yang Deng, Zhen Jia, Feimei Yang

“…Synchronization of multilayer complex networks is one of the important frontier issues in network science. In this paper, we strictly derived the analytic expressions of the eigenvalue spectrum of multilayer star and star-ring networks and analyzed the synchronizability of these two networks by using the master stability function (MSF) theory. …”
Get full text

Remote Satellites Computing transmission and Key Technologies by lang HuYan, Ying Li, Quan Zhou, Jiayuan Wei, Juanni Liu, Yi Zhang

“…The diversity of remote sensing data and the increase of acquisition ability make the amount of data generated on the satellite increase geometrically.This situation puts tremendous pressure on the remote satellite data transmission system.This makes the remote satellite data transmission system unable to meet the needs of real-time transmission and becomes the bottleneck of remote sensing data application.The research on massive remote satellite data transmission systems and technology is one of the frontier fi elds of international satellite information network science and technology.Focused on the critical problems of remote satellite real-time data transmission bottleneck, remote sensing data transmission timeliness, remote sensing load utilization, and onboard storage, combined with the traditional satellite data transmission system with onboard intelligent processing, a new remote satellite computing transmission framework was proposed, and its key technologies were analyzed.…”
Get full text

Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. by Lang Chai, Rui Huang

“…Link prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduces a novel algorithm named SW-Metapath2vec. …”
Get full text

Multiscale network alignment model based on convolution of homogeneous multilayer graphs by CUI Jiahao, JIANG Tao, XU Mengyao

“…Social network alignment as an important research method in network science has been widely used in several fields. …”
Get full text

Immunization of Cooperative Spreading Dynamics on Complex Networks by Jun Wang, Shi-Min Cai, Tao Zhou

“…Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. …”
Get full text

Challenges and opportunities for digital twins in precision medicine from a complex systems perspective by Manlio De Domenico, Luca Allegri, Guido Caldarelli, Valeria d’Andrea, Barbara Di Camillo, Luis M. Rocha, Jordan Rozum, Riccardo Sbarbati, Francesco Zambelli

“…It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.…”
Get full text

Hyperedge overlap drives explosive transitions in systems with higher-order interactions by Federico Malizia, Santiago Lamata-Otín, Mattia Frasca, Vito Latora, Jesús Gómez-Gardeñes

Get full text

Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering by Yi-Kun Zhao, Guo-Qing Wang, Xiao-Xiao Zhan, Peng-Hui Yang

“…Therefore, this paper uses network science to build a dynamic network to analyze the similarity of music, the evolution process, and the impact of music on culture, which has certain research significance and practical value in the fields of music, history, social science, and practice.…”
Get full text

The dynamics of higher-order novelties by Gabriele Di Bona, Alessandro Bellina, Giordano De Marzo, Angelo Petralia, Iacopo Iacopini, Vito Latora

Get full text

Firing propagation in empirical cognitive networks of human brain by Dehua Chen, Ruohua Gao, Zhiyin Yang, Siyu Huo, Zonghua Liu

“…Understanding the physical mechanisms of brain functions has always been a challenging problem in the fields of nonlinear dynamics and network science. A promising approach to address this problem is by studying signal propagation on brain cognitive networks. …”
Get full text

The symmetric division Szeged index: A novel tool for predicting physical and chemical properties of complex networks by Modjtaba Ghorbani, Zahra Vaziri, Razie Alidehi-Ravandi, Yilun Shang

“…As research progresses, we anticipate further developments and applications that will enhance our comprehension of complex systems and their properties across fields like network science, computer science, and physics.…”
Get full text

Further Results on Resistance Distance and Kirchhoff Index in Electric Networks by Qun Liu, Jia-Bao Liu, Jinde Cao

Get full text

Spatial-temporal analysis of the international trade network by Donghai Liu, Ziwen Yang, Kun Qin, Kai Li

“…With the support of spatial-temporal data analysis technologies and network science, the International Trade Network (ITN) research has made significant progress, demonstrating broad application prospects in mining market evolution and predicting trade dynamics. …”
Get full text

Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attribut... by Srebrenka Letina, Tessa F. Blanken, Marie K. Deserno, Denny Borsboom

“…The main idea of this paper is that a psychological network entails more potentially useful and interesting information that can be reaped by other methods widely used in network science. Specifically, we suggest methods that provide clearer picture about hierarchical arrangement of nodes in the network, address heterogeneity of nodes in the network, and look more closely at network’s local structure. …”
Get full text

Study on the reliability of hypergraphs based on non-backtracking matrix centrality by Hao PENG, Cheng QIAN, Dandan ZHAO, Ming ZHONG, Jianmin HAN, Ziyi XIE, Wei WANG

“…In recent years, there has been widespread attention on hypergraphs as a research hotspot in network science.The unique structure of hypergraphs, which differs from traditional graphs, is characterized by hyperedges that can connect multiple nodes simultaneously, resulting in more complex and higher-order relationships.Effectively identifying important nodes and hyperedges in such network structures poses a key challenge.Eigenvector centrality, a common metric, has limitations in its application due to its locality when dealing with hub nodes with extremely high degree values in the network.To address this issue, the hypergraphs were transformed into their corresponding line graphs, and non-backtracking matrix centrality was employed as a method to measure the importance of hyperedges.This approach demonstrated better uniformity and differentiation in assessing the importance of hyperedges.Furthermore, the application of both eigenvector centrality and non-backtracking matrix centrality in assessing the importance of nodes in hypergraphs was explored.Comparative analysis revealed that non-backtracking matrix centrality effectively distinguished the importance of nodes.This research encompassed theoretical analysis, model construction, and empirical studies on real-world data.To validate the proposed method and conclusion, six real-world hypergraphs were selected as experimental subjects.The application of these methods to these hypergraphs confirmed the effectiveness of non-backtracking matrix centrality in identifying important nodes and hyperedges.The findings of this research offer a fresh perspective and approach for identifying key elements in hypergraphs, holding significant theoretical and practical implications for understanding and analyzing complex network systems.…”
Get full text

A Linear Algebraic Framework for Dynamic Scheduling Over Memory-Equipped Quantum Networks by Paolo Fittipaldi, Anastasios Giovanidis, Frederic Grosshans

“…The framework is then employed to apply Lyapunov drift minimization (a standard technique in classical network science) to mathematically derive a natural class of scheduling policies for quantum networks minimizing the square norm of the user demand backlog. …”
Get full text

Link prediction method for dynamic networks based on matching degree of nodes by Cong LI, Xinsheng JI, Ushuxin LI, Jinsong LI, Haitao LI

“…The research of dynamic evolutionary trends and temporal characteristics in real networks which are important part of the real world is a hot research issue nowadays.As a typical research tool in the field of network science, link prediction technique can be used to predict the network evolution by mining the historical edge information and then predict the future edge.The topological evolution of dynamic real networks was analyzed and it found that the interaction and matching between nodes in the network topology can capture the dynamic characteristics of the network more comprehensively.The proposed method analyzed the attribute characteristics of network nodes, and defined a node importance quantification method based on primary and secondary influences.Besides, a time decay factor was introduced to portray the influence of network topology on the formation of connected edges at different moments.Furthermore, the node importance and time decay factor were combined to define the Temporal Matching Degree of Nodes (TMDN), which was used to measure the possibility of future edge formation between node pairs.The experimental results in five real dynamic network datasets showed that the proposed method achieves better prediction performance under both AUC and Ranking Score, with a maximum improvement of 42%.It also proved the existence of interactive matching priority among nodes, and confirmed the effectiveness of both primary and secondary influence of nodes.As the future work, we will add diversified feature information to further deepen the analysis of dynamic real networks and then predict the evolution law more accurately.…”
Get full text

Identifying key genes in cancer networks using persistent homology by Rodrigo Henrique Ramos, Yago Augusto Bardelotte, Cynthia de Oliveira Lage Ferreira, Adenilso Simao

“…Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. …”
Get full text

Abnormal link detection algorithm based on semi-local structure by Haoran SHI, Lixin JI, Shuxin LIU, Gengrun WANG

“…With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy.…”
Get full text