標(biāo)題: Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The [打印本頁(yè)] 作者: Dangle 時(shí)間: 2025-3-21 16:25
書目名稱Complex Networks & Their Applications XII影響因子(影響力)
書目名稱Complex Networks & Their Applications XII影響因子(影響力)學(xué)科排名
書目名稱Complex Networks & Their Applications XII網(wǎng)絡(luò)公開(kāi)度
書目名稱Complex Networks & Their Applications XII網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Complex Networks & Their Applications XII被引頻次
書目名稱Complex Networks & Their Applications XII被引頻次學(xué)科排名
書目名稱Complex Networks & Their Applications XII年度引用
書目名稱Complex Networks & Their Applications XII年度引用學(xué)科排名
書目名稱Complex Networks & Their Applications XII讀者反饋
書目名稱Complex Networks & Their Applications XII讀者反饋學(xué)科排名
作者: 大吃大喝 時(shí)間: 2025-3-21 22:01
Bayesian Hierarchical Network Autocorrelation Models for?Modeling the?Diffusion of?Hospital-Level Quto as social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed. We develop two hierarchical network autocorrelation models to represent peer effects between hospitals when modeling individual outcomes of the patients who attend those作者: Basilar-Artery 時(shí)間: 2025-3-22 02:27 作者: Foreshadow 時(shí)間: 2025-3-22 06:29
Does Isolating High-Modularity Communities Prevent Cascading Failure?y partitions. It seems obvious that isolating high-modularity communities is a good way to prevent the spread of cascading failures. Here we develop a heuristic approach informed by Moore-Shannon network reliability that focuses on dynamics rather than topology. It defines communities directly in te作者: 嫌惡 時(shí)間: 2025-3-22 08:47
Two to?Five Truths in?Non-negative Matrix Factorizationent a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of [.], where the authors proved adjacency spectral embedding (ASE) spectral clustering was作者: LATHE 時(shí)間: 2025-3-22 15:16 作者: LATHE 時(shí)間: 2025-3-22 21:02 作者: Exclude 時(shí)間: 2025-3-22 22:49
Dual Communities Characterize Structural Patterns and?Robustness in?Leaf Venation Networks analysis can be complemented by the definition of communities in the networks’ plane dual. Such communities are characterized not by weak but by strong connectivity between the different components of the network. We extract dual communities in leaf venation networks, finding that they can reveal s作者: 試驗(yàn) 時(shí)間: 2025-3-23 02:42
Tailoring Benchmark Graphs to?Real-World Networks for?Improved Prediction of?Community Detection Per algorithm and trust the generated results without further investigation, but algorithm performance can vary depending on the network characteristics. We demonstrate that by running experiments on benchmark graphs tailored to match characteristics of a real-world network of interest, a better unders作者: attenuate 時(shí)間: 2025-3-23 09:09
Network Based Methodology for?Characterizing Interdisciplinary Expertise in?Emerging Research-world problems. Understanding characteristics of IDR early (as soon as projects get funded), can formatively shape a research community at portfolio, project, and individual investigator levels. This involves surfacing the interacting components and the context that manifest IDR. We present a netwo作者: 入會(huì) 時(shí)間: 2025-3-23 11:15
Classification Supported by?Community-Aware Node Featuresnetworks, affecting properties of their nodes. In this paper, we propose a family of community-aware node features and then investigate their properties. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered compl作者: Diaphragm 時(shí)間: 2025-3-23 16:35 作者: ellagic-acid 時(shí)間: 2025-3-23 19:37 作者: 心胸開(kāi)闊 時(shí)間: 2025-3-24 00:09
Detecting Community Structures in?Patients with?Peripheral Nervous System Disordersomes even more formidable in bipartite networks. The focus of this study is the patients with problems in their Peripheral Nerve System. To this aim, we engaged the assistance of spinal specialty clinics in the collection of necessary Data. We employ the bipartite network to represent the relationsh作者: 顧客 時(shí)間: 2025-3-24 05:10
Community Detection in?Feature-Rich Networks Using Gradient Descent Approachtegy to recover communities in feature-rich networks. Our adoption of this strategy did not lead to promising results, and thus to improve them, we propose a special “refinement” mechanism, which culls out potentially misleading objects during the optimization. We validated and compared our proposed作者: 旅行路線 時(shí)間: 2025-3-24 09:30
Detecting Strong Cliques in?Co-authorship Networkstures representing a small group of people or other entities who share common characteristics and know each other. Clique detection algorithms can be applied in all domains where networks are used to describe relationships among entities. That is not only in social, information, or communication net作者: 極少 時(shí)間: 2025-3-24 11:16
Mosaic Benchmark Networks: Modular Link Streams for?Testing Dynamic Community Detection Algorithmsighly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remain作者: 壯觀的游行 時(shí)間: 2025-3-24 17:37
Entropic Detection of?Chromatic Community Structuresns of people, molecules or processes within a network. The issue is to provide a network partition representative of this organization so that each community presumably gathers nodes sharing a common mission, purpose or property. Usually, this identification is based on the difference in connectivit作者: aqueduct 時(shí)間: 2025-3-24 19:49 作者: 不安 時(shí)間: 2025-3-25 03:12 作者: Notorious 時(shí)間: 2025-3-25 05:07
Signature-Based Community Detection for?Time Seriesnderlying time series is manipulated..This research contributes to the field of community detection by introducing a signature-based similarity measure, offering an alternative to conventional correlation matrices.作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-25 10:57 作者: CAMEO 時(shí)間: 2025-3-25 11:54
Detecting Strong Cliques in?Co-authorship Networksry clique detected by traditional algorithms truly satisfies the sociological assumption above. Informally speaking, the approach presented in this paper assumes that each pair of clique nodes must be closer to each other and other clique nodes than to non-clique nodes. Using experiments with weight作者: 一窩小鳥(niǎo) 時(shí)間: 2025-3-25 18:44
Mosaic Benchmark Networks: Modular Link Streams for?Testing Dynamic Community Detection Algorithmsequently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.作者: 同義聯(lián)想法 時(shí)間: 2025-3-26 00:01 作者: 裁決 時(shí)間: 2025-3-26 02:18 作者: 坦白 時(shí)間: 2025-3-26 08:12 作者: Limerick 時(shí)間: 2025-3-26 09:43
https://doi.org/10.1007/88-470-0382-2 for knowledge integration, specifically within “hotspot” topics. It also reveals gaps in IDR potential. Applying our network-based methodology for understanding IDR could enable other research domains and communities to conduct early and rapid analyses of the emerging IDR profile in their network, 作者: Medicaid 時(shí)間: 2025-3-26 15:31 作者: lymphedema 時(shí)間: 2025-3-26 17:25 作者: DAUNT 時(shí)間: 2025-3-26 21:08
https://doi.org/10.1007/88-470-0382-2ry clique detected by traditional algorithms truly satisfies the sociological assumption above. Informally speaking, the approach presented in this paper assumes that each pair of clique nodes must be closer to each other and other clique nodes than to non-clique nodes. Using experiments with weight作者: Pituitary-Gland 時(shí)間: 2025-3-27 01:29
https://doi.org/10.1007/88-470-0382-2equently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.作者: 檢查 時(shí)間: 2025-3-27 09:17 作者: Insul島 時(shí)間: 2025-3-27 12:45 作者: labile 時(shí)間: 2025-3-27 14:58
https://doi.org/10.1007/978-3-8348-9399-4to as social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed. We develop two hierarchical network autocorrelation models to represent peer effects between hospitals when modeling individual outcomes of the patients who attend those作者: FLAT 時(shí)間: 2025-3-27 20:09 作者: TRACE 時(shí)間: 2025-3-28 01:02
Marina V. Plekhanova,Guzel D. Baybulatovay partitions. It seems obvious that isolating high-modularity communities is a good way to prevent the spread of cascading failures. Here we develop a heuristic approach informed by Moore-Shannon network reliability that focuses on dynamics rather than topology. It defines communities directly in te作者: thrombus 時(shí)間: 2025-3-28 02:28
Murat Ad?var,Youssef N. Raffoulent a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of [.], where the authors proved adjacency spectral embedding (ASE) spectral clustering was作者: ADORN 時(shí)間: 2025-3-28 07:02 作者: Aphorism 時(shí)間: 2025-3-28 11:42 作者: Ptsd429 時(shí)間: 2025-3-28 14:51
,Stabilità e controllo neuroumorale, analysis can be complemented by the definition of communities in the networks’ plane dual. Such communities are characterized not by weak but by strong connectivity between the different components of the network. We extract dual communities in leaf venation networks, finding that they can reveal s作者: hypertension 時(shí)間: 2025-3-28 20:00
https://doi.org/10.1007/88-470-0382-2 algorithm and trust the generated results without further investigation, but algorithm performance can vary depending on the network characteristics. We demonstrate that by running experiments on benchmark graphs tailored to match characteristics of a real-world network of interest, a better unders作者: aquatic 時(shí)間: 2025-3-29 00:26
https://doi.org/10.1007/88-470-0382-2-world problems. Understanding characteristics of IDR early (as soon as projects get funded), can formatively shape a research community at portfolio, project, and individual investigator levels. This involves surfacing the interacting components and the context that manifest IDR. We present a netwo作者: 認(rèn)為 時(shí)間: 2025-3-29 03:23
,Stabilità e controllo neuroumorale,networks, affecting properties of their nodes. In this paper, we propose a family of community-aware node features and then investigate their properties. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered compl作者: Brocas-Area 時(shí)間: 2025-3-29 10:47 作者: 初學(xué)者 時(shí)間: 2025-3-29 13:14
https://doi.org/10.1007/88-470-0382-2ization, where vertices split into groups that further subdivide across multiple scales. However, individuals in social networks typically belong to multiple communities due to their various affiliations, such as family, friends, and colleagues. These overlaps will emerge in the community structure 作者: Campaign 時(shí)間: 2025-3-29 19:05 作者: macabre 時(shí)間: 2025-3-29 21:31
https://doi.org/10.1007/88-470-0382-2tegy to recover communities in feature-rich networks. Our adoption of this strategy did not lead to promising results, and thus to improve them, we propose a special “refinement” mechanism, which culls out potentially misleading objects during the optimization. We validated and compared our proposed作者: EXALT 時(shí)間: 2025-3-30 00:28 作者: Tinea-Capitis 時(shí)間: 2025-3-30 07:16
https://doi.org/10.1007/88-470-0382-2ighly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remain作者: Mortal 時(shí)間: 2025-3-30 11:07 作者: Contracture 時(shí)間: 2025-3-30 12:32
Hocine Cherifi,Luis M. Rocha,Murat DonduranPresents the latest research in Complex Networks and their Applications.Gathers the edited proceedings of the Twelfth International Workshop on Complex Networks & their Applications.Offers state-of-th作者: pacifist 時(shí)間: 2025-3-30 19:02
Studies in Computational Intelligencehttp://image.papertrans.cn/c/image/231488.jpg作者: 聾子 時(shí)間: 2025-3-30 23:24 作者: hypotension 時(shí)間: 2025-3-31 04:34
https://doi.org/10.1007/978-3-031-53499-7Complex Networks; Complex Networks 2023; Network Models; Network Dynamics; Network Analysis作者: oblique 時(shí)間: 2025-3-31 07:39 作者: 帽子 時(shí)間: 2025-3-31 11:56 作者: 發(fā)生 時(shí)間: 2025-3-31 14:18
Marina V. Plekhanova,Guzel D. Baybulatovarms of the size of cascades they allow. We demonstrate that isolating communities defined this way may control cascading failure better. Moreover, this approach is sensitive to the values of dynamical parameters and allows for problem-specific constraints such as cost.作者: 招人嫉妒 時(shí)間: 2025-3-31 20:54
,Stabilità e controllo neuroumorale,tructural features not visible to traditional community detection methods. Furthermore, we show that the presence of dual community structures suppresses failure spreading and that dual communities can be used to classify different leaf types.作者: 褪色 時(shí)間: 2025-3-31 22:27 作者: ACTIN 時(shí)間: 2025-4-1 05:36
Conference proceedings 2024te on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2023). The carefully selected papers cover a wide range of theoretical topics such as networ作者: 直覺(jué)好 時(shí)間: 2025-4-1 06:36 作者: 用肘 時(shí)間: 2025-4-1 10:12 作者: inventory 時(shí)間: 2025-4-1 17:33
Classification Supported by?Community-Aware Node Featureses. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered completely neither by classical node features nor by classical or structural node embeddings.作者: 的闡明 時(shí)間: 2025-4-1 22:20
https://doi.org/10.1057/9780230306981thin networks, while results on synthetic networks show that the CM algorithm improves accuracy in recovering true communities. Our study also raises questions about the “clusterability” of networks and mathematical models of community structure.
