標(biāo)題: Titlebook: Complex Networks & Their Applications IX; Volume 1, Proceeding Rosa M. Benito,Chantal Cherifi,Marta Sales-Pardo Conference proceedings 2021 [打印本頁(yè)] 作者: 老鼠系領(lǐng)帶 時(shí)間: 2025-3-21 17:57
書(shū)目名稱Complex Networks & Their Applications IX影響因子(影響力)
書(shū)目名稱Complex Networks & Their Applications IX影響因子(影響力)學(xué)科排名
書(shū)目名稱Complex Networks & Their Applications IX網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Complex Networks & Their Applications IX網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Complex Networks & Their Applications IX被引頻次
書(shū)目名稱Complex Networks & Their Applications IX被引頻次學(xué)科排名
書(shū)目名稱Complex Networks & Their Applications IX年度引用
書(shū)目名稱Complex Networks & Their Applications IX年度引用學(xué)科排名
書(shū)目名稱Complex Networks & Their Applications IX讀者反饋
書(shū)目名稱Complex Networks & Their Applications IX讀者反饋學(xué)科排名
作者: majestic 時(shí)間: 2025-3-21 23:09 作者: parallelism 時(shí)間: 2025-3-22 02:34
1860-949X on Complex Networks and their Applications (COMPLEX NETWORK.This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer作者: 手工藝品 時(shí)間: 2025-3-22 07:15 作者: Monolithic 時(shí)間: 2025-3-22 10:24 作者: isotope 時(shí)間: 2025-3-22 13:47 作者: isotope 時(shí)間: 2025-3-22 17:04 作者: extinguish 時(shí)間: 2025-3-22 22:23 作者: 試驗(yàn) 時(shí)間: 2025-3-23 02:16 作者: 驚奇 時(shí)間: 2025-3-23 07:15
Difference Equations with Continuous Time, symmetrized version of the adjacency matrices. A simulation study with a directed stochastic block model shows that directed spectral clustering can succeed where the symmetrized approach fails. And we find interesting and informative differences between the two approaches in the application to Congressional cosponsorship data.作者: slipped-disk 時(shí)間: 2025-3-23 11:39
https://doi.org/10.1007/978-3-319-02417-2a pioneering non-manual parameter tuning scheme that provides the equal impact of structure and attributes on the CD results. Experiments with synthetic and real-world ASNs show that our conclusions help to reasonably interpret the CD results and that our tuning scheme is very accurate.作者: 友好關(guān)系 時(shí)間: 2025-3-23 17:41
https://doi.org/10.1007/978-3-540-74775-8struct co-sponsorship networks at the UNGA and use the Leiden algorithm to identify community clusters. Our multiclass random forest classification model supports our assumption and shows that regime type is associated with cooperation clusters in UNGA co-sponsorship networks.作者: 看法等 時(shí)間: 2025-3-23 21:29
Efficient Community Detection by?Exploiting Structural Properties of?Real-World User-Item Graphs(with respect to the number of vertices) for processing the entire graph, which makes it highly practical for processing large-scale graphs which typically arise in real-world applications. The performance of the proposed algorithm, in terms of both community-detection accuracy and efficiency, is experimentally evaluated with real-world datasets.作者: 阻塞 時(shí)間: 2025-3-24 01:22
Effects of Community Structure in Social Networks on Speed of Information Diffusionckle the tasks of predicting time intervals between a first tweet and its .-th retweet. We show the potential of community structure features in a social network for predicting information diffusion speed.作者: Gustatory 時(shí)間: 2025-3-24 02:52
Spectral Clustering for Directed Networks symmetrized version of the adjacency matrices. A simulation study with a directed stochastic block model shows that directed spectral clustering can succeed where the symmetrized approach fails. And we find interesting and informative differences between the two approaches in the application to Congressional cosponsorship data.作者: Comprise 時(shí)間: 2025-3-24 08:17
Composite Modularity and Parameter Tuning in the Weight-Based Fusion Model for Community Detection ia pioneering non-manual parameter tuning scheme that provides the equal impact of structure and attributes on the CD results. Experiments with synthetic and real-world ASNs show that our conclusions help to reasonably interpret the CD results and that our tuning scheme is very accurate.作者: nepotism 時(shí)間: 2025-3-24 13:07 作者: 考得 時(shí)間: 2025-3-24 18:48
Stability of Linear Autonomous Equations to optimize, we apply a greedy-wise algorithm for detecting communities in sequence. We experimentally show that our proposed method is effective on both real-world data and synthetic data. In the cases at which attributes are categorical, we compare our approach with state-of-the-art algorithms. Our algorithm appears competitive against them.作者: gerrymander 時(shí)間: 2025-3-24 22:22 作者: 一起平行 時(shí)間: 2025-3-25 02:30
Linear Differential Delay Equations,ric model that flexibly estimates the number of blocks and takes into account the possibility of unseen nodes. Using one synthetic dataset and one real-world stock ownership dataset, we show that our model outperforms state-of-the-art SBMs for held-out link prediction tasks.作者: 致敬 時(shí)間: 2025-3-25 05:05 作者: Spinal-Tap 時(shí)間: 2025-3-25 07:44 作者: Grievance 時(shí)間: 2025-3-25 13:08 作者: 使增至最大 時(shí)間: 2025-3-25 16:57 作者: 憂傷 時(shí)間: 2025-3-25 21:15 作者: CALL 時(shí)間: 2025-3-26 01:34
1860-949X eading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks and technological networks.?..?.978-3-030-65349-1978-3-030-65347-7Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: TOXIC 時(shí)間: 2025-3-26 05:37
https://doi.org/10.1007/978-1-4613-0385-5y the authors, which allows the operator to carry out the procedures required for the method, visualize the results and export the obtained data, are presented. The third part shows the application of the “core method” on a weighted graph, based on the data about the coverage of the activities of th作者: 填滿 時(shí)間: 2025-3-26 09:01 作者: Aids209 時(shí)間: 2025-3-26 13:42
Center manifolds in Banach spacese can say that the preference is stronger when . is greater than 0.5, and a value of . between 0.20 and 0.80. The third parameter ., which controls the intensity of community membership, defines the degree of relationship of a node to a community. The communities detected by the preference implicati作者: 欺騙手段 時(shí)間: 2025-3-26 17:19 作者: Sciatica 時(shí)間: 2025-3-26 21:39 作者: Flu表流動(dòng) 時(shí)間: 2025-3-27 01:21 作者: Angioplasty 時(shí)間: 2025-3-27 07:15
Core Method for Community Detectiony the authors, which allows the operator to carry out the procedures required for the method, visualize the results and export the obtained data, are presented. The third part shows the application of the “core method” on a weighted graph, based on the data about the coverage of the activities of th作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-27 10:28
Community Detection in a Multi-layer Network Over Social Mediacebook page. The study also investigates how strong the ties between users and their polarity towards the page over the span of time. The results successfully remove the isolates from the network and built a well-defined structure of the community.作者: hazard 時(shí)間: 2025-3-27 15:03
Using Preference Intensity for Detecting Network Communitiese can say that the preference is stronger when . is greater than 0.5, and a value of . between 0.20 and 0.80. The third parameter ., which controls the intensity of community membership, defines the degree of relationship of a node to a community. The communities detected by the preference implicati作者: 裙帶關(guān)系 時(shí)間: 2025-3-27 18:42 作者: Cardioversion 時(shí)間: 2025-3-27 23:03
Local Community Detection Algorithm with Self-defining Source Nodesers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains more over networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiment作者: Nuance 時(shí)間: 2025-3-28 02:43
Investigating Centrality Measures in Social Networks with Community Structure, and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary作者: 邊緣 時(shí)間: 2025-3-28 08:51 作者: 壓艙物 時(shí)間: 2025-3-28 12:07
Efficient Community Detection by?Exploiting Structural Properties of?Real-World User-Item Graphsn a user and an item. Instead of developing a generic clustering algorithm for arbitrary graphs, we tailor our algorithm for user-item graphs by taking advantage of the inherent structural properties that exist in real-world networks. Assuming the existence of the core-periphery structure that has b作者: 哀悼 時(shí)間: 2025-3-28 16:13 作者: mechanism 時(shí)間: 2025-3-28 20:45 作者: LUCY 時(shí)間: 2025-3-29 02:54 作者: Organonitrile 時(shí)間: 2025-3-29 06:30 作者: Carcinoma 時(shí)間: 2025-3-29 09:23
Nondiagonal Mixture of Dirichlet Network Distributions for Analyzing a Stock Ownership Networkades. However, the SBM is limited in analyzing complex networks as the model is, in essence, a random graph model that cannot reproduce the basic properties of many complex networks, such as sparsity and heavy-tailed degree distribution. In this paper, we provide an edge exchangeable block model tha作者: Parley 時(shí)間: 2025-3-29 14:28
Spectral Clustering for Directed Networksed clustering of nodes. Spectral methods give well-established approaches to the problem in the undirected setting; however, they generally do not account for edge directionality. We consider a directed spectral method that utilizes a graph Laplacian defined for non-symmetric adjacency matrices. We 作者: inscribe 時(shí)間: 2025-3-29 16:36 作者: 漂泊 時(shí)間: 2025-3-29 20:51 作者: 有雜色 時(shí)間: 2025-3-30 00:39
Community Detection in a Multi-layer Network Over Social Mediammunity detection is to identify strongly connected components in a complex network. It reveals how people connect and interact with each other. In the real world, however, a person is engaged in several traits of connections, these connections or social ties carry other different challenges in comm作者: 紅潤(rùn) 時(shí)間: 2025-3-30 04:33
Using Preference Intensity for Detecting Network Communities and this defines sub-communities of varying size. In this paper, we propose a preference implication based-method for generating overlapping structures based on a local function optimization approach. We introduce some parameters in our novel method to design the communities according to a threshol作者: vascular 時(shí)間: 2025-3-30 11:00
Towards Causal Explanations of Community Detection in Networksce - termed communities. Our goal is to further study this problem from a different perspective related to the questions of the cause of belongingness to a community. To this end, we apply the framework of causality and responsibility developed by Halpern and Pearl?[.]. We provide an algorithm-semi-作者: Cuisine 時(shí)間: 2025-3-30 14:11
A Pledged Community? Using Community Detection to Analyze Autocratic Cooperation in UN Co-sponsorshiliable and do not sign agreements with them. While it is challenging to capture autocratic cooperation with traditional approaches such as signed alliance treaties, co-sponsorship at the United Nations General Assembly (UNGA) offers a valuable alternative. UNGA co-sponsorship is less binding than al作者: 埋葬 時(shí)間: 2025-3-30 17:37
Distances on a Graphto the actual clustering, our goal is to find a distance whose pairwise minimization will lead to densely connected clusters. Our thesis is centered on the widely accepted notion that strong clusters are sets of vertices with high induced subgraph density. We posit that vertices sharing more connect作者: 蜿蜒而流 時(shí)間: 2025-3-30 22:09
Local Community Detection Algorithm with Self-defining Source Nodesorithms. Considering the growing size of existing networks, . community detection methods have gained attention in contrast to . methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a giv作者: LINE 時(shí)間: 2025-3-31 01:00
Investigating Centrality Measures in Social Networks with Community Structurewith this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world
