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: CULT 時(shí)間: 2025-3-21 16:42
書(shū)目名稱(chēng)Complex Networks & Their Applications XII影響因子(影響力)
書(shū)目名稱(chēng)Complex Networks & Their Applications XII影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Complex Networks & Their Applications XII網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Complex Networks & Their Applications XII網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Complex Networks & Their Applications XII被引頻次
書(shū)目名稱(chēng)Complex Networks & Their Applications XII被引頻次學(xué)科排名
書(shū)目名稱(chēng)Complex Networks & Their Applications XII年度引用
書(shū)目名稱(chēng)Complex Networks & Their Applications XII年度引用學(xué)科排名
書(shū)目名稱(chēng)Complex Networks & Their Applications XII讀者反饋
書(shū)目名稱(chēng)Complex Networks & Their Applications XII讀者反饋學(xué)科排名
作者: Vasodilation 時(shí)間: 2025-3-21 23:54 作者: acetylcholine 時(shí)間: 2025-3-22 03:59 作者: Culpable 時(shí)間: 2025-3-22 06:36
978-3-031-53470-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: fabricate 時(shí)間: 2025-3-22 09:22 作者: Default 時(shí)間: 2025-3-22 16:33 作者: Default 時(shí)間: 2025-3-22 17:37
https://doi.org/10.1007/978-3-322-94647-8ex relational data. Large real-world graphs, characterised by sparsity in relations and features, necessitate dedicated tools that existing dense tensor-centred approaches cannot easily provide. To address this need, we introduce a GNNs module in Scikit-network, a Python package for graph analysis, 作者: 能量守恒 時(shí)間: 2025-3-23 00:34
https://doi.org/10.1007/978-3-658-16596-3in its early stages. In our research, we repurpose GNN Graph Classification, traditionally rooted in disciplines like biology and chemistry, to delve into the intricacies of time series datasets. We demonstrate how graphs are constructed within individual time series and across multiple datasets, hi作者: ESPY 時(shí)間: 2025-3-23 04:26 作者: GEAR 時(shí)間: 2025-3-23 09:27
https://doi.org/10.1007/978-3-658-12285-0such as their .. However, if this were true, modifying only the training procedure for a given architecture would not likely to enhance performance. Contrary to this belief, our paper demonstrates several ways to achieve such improvements. We begin by highlighting the training challenges of GCNs fro作者: heckle 時(shí)間: 2025-3-23 12:35
https://doi.org/10.1007/978-3-658-12285-0onalized and relevant recommendations. However, existing works adapting Graph Neural Networks (GNN) to recommendations struggle with the cold-start problem. Indeed, it is difficult to make accurate recommendations for new users or items with little or no interaction data. Building on previous work, 作者: 緯線 時(shí)間: 2025-3-23 14:04 作者: 歌唱隊(duì) 時(shí)間: 2025-3-23 21:07
https://doi.org/10.1007/978-3-642-95273-9eries datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, 作者: white-matter 時(shí)間: 2025-3-24 01:09 作者: Aphorism 時(shí)間: 2025-3-24 03:13
Die Instabilit?t von Marktprozessenof the connectivity and feature information spaces of graphs. Identifying the latent communities has many practical applications from social networks to genomics. Current benchmarks of real world performance are confusing due to the variety of decisions influencing the evaluation of GNNs at this tas作者: 嘲弄 時(shí)間: 2025-3-24 07:41
Die Rolle der Einkommenspolitikare often measured using a co-authorship network, which is the projection of a bipartite author-paper network. Caution is required when interpreting the edge weights that appear in such projections. However, backbone models offer a solution by providing a formal statistical method for evaluating whe作者: 售穴 時(shí)間: 2025-3-24 11:00
https://doi.org/10.1007/978-3-322-90813-1es which favor minority attribute nodes and which give preference to “more popular” nodes having higher centrality measures in the network. A data study shows the efficiency of Page-rank and walk-based network sampling schemes on a directed network model and a real-world network with small minoritie作者: Accomplish 時(shí)間: 2025-3-24 15:27
https://doi.org/10.1007/978-3-322-90813-1 models. In link prediction, the task is to predict missing or future links in a given network dataset. We focus on the pretrained setting, in which such a predictive model is trained on one dataset, and employed on another dataset. The framework allows one to overcome a number of nontrivial challen作者: 兇兆 時(shí)間: 2025-3-24 22:26 作者: Generosity 時(shí)間: 2025-3-25 02:24
,Grunds?tzliches über Stabilit?tsprobleme,are essential. Hospitals employ various methods and devices to ensure the patient’s well-being. However, many of these efforts are compromised when the observation is neglected. This paper presents an effective approach to enable doctors (or) supervisors to track and forecast the patient’s health co作者: Inkling 時(shí)間: 2025-3-25 07:03 作者: GRE 時(shí)間: 2025-3-25 10:18 作者: 摘要 時(shí)間: 2025-3-25 14:44
E-MIGAN: Tackling Cold-Start Challenges in?Recommender Systems pairwise interactions, and iii) A Content-Based Embedding model, which overcomes the cold start issue. The empirical study on real-world datasets proves that E-MIGAN achieves state-of-the-art performance, demonstrating its effectiveness in capturing complex interactions in graph-structured data.作者: 抵消 時(shí)間: 2025-3-25 17:27 作者: tinnitus 時(shí)間: 2025-3-25 21:07
A Framework for?Empirically Evaluating Pretrained Link Prediction Models on. Moreover, we systematically assessed the relationship between topological similarity and performance difference of pretrained models and a model trained on the same data. We find that similar network pairs in terms of clustering coefficient, and to a lesser extent degree assortativity and gini 作者: 盡管 時(shí)間: 2025-3-26 03:13
Efficient Approach for Patient Monitoring: ML-Enabled Framework with Smart Connected Systemsg range detection techniques supported by Bluetooth master-slave communication. Computations are performed in the backend such that the alerts are notified based on the conditions assigned to the respective patients. In case of emergency, we can reliably predict the condition of a patient with impro作者: Synthesize 時(shí)間: 2025-3-26 05:18 作者: 放氣 時(shí)間: 2025-3-26 11:50
https://doi.org/10.1007/978-3-658-16596-3s in data interpretation. This research lays the groundwork for integrating these methodologies, indicating vast potential for their wider application and opening up promising avenues for future exploration.作者: 出沒(méi) 時(shí)間: 2025-3-26 14:18
https://doi.org/10.1007/978-3-658-12285-0 pairwise interactions, and iii) A Content-Based Embedding model, which overcomes the cold start issue. The empirical study on real-world datasets proves that E-MIGAN achieves state-of-the-art performance, demonstrating its effectiveness in capturing complex interactions in graph-structured data.作者: 鋼筆記下懲罰 時(shí)間: 2025-3-26 18:32 作者: Constituent 時(shí)間: 2025-3-26 23:13
https://doi.org/10.1007/978-3-322-90813-1 on. Moreover, we systematically assessed the relationship between topological similarity and performance difference of pretrained models and a model trained on the same data. We find that similar network pairs in terms of clustering coefficient, and to a lesser extent degree assortativity and gini 作者: Meditate 時(shí)間: 2025-3-27 02:18
,Grunds?tzliches über Stabilit?tsprobleme,g range detection techniques supported by Bluetooth master-slave communication. Computations are performed in the backend such that the alerts are notified based on the conditions assigned to the respective patients. In case of emergency, we can reliably predict the condition of a patient with impro作者: Density 時(shí)間: 2025-3-27 08:14
https://doi.org/10.1007/978-3-322-94647-8t (a) edits the highest scoring edges and (b) re-embeds the edited graph to refresh gradients, leading to less biased edge choices. We empirically study ORE through a set of proposed design tasks, each with an external validation method, demonstrating that ORE improves upon previous methods by up to 50%.作者: Bernstein-test 時(shí)間: 2025-3-27 09:37 作者: fodlder 時(shí)間: 2025-3-27 15:24
https://doi.org/10.1007/978-3-642-59046-7le training data while misleading the targeted classifier. Importantly, our method does not assume any knowledge about the underlying architecture. Finally, we validate the effectiveness of our proposed method in a realistic setting related to molecular graphs.作者: erythema 時(shí)間: 2025-3-27 20:14 作者: 急急忙忙 時(shí)間: 2025-3-28 01:56
Stabilit?tsprobleme der Elastostatikrelations corpus for predicting BEFORE, AFTER and OVERLAP links with event graph for correct set of relations. Comparison with various Biomedical-BERT embedding types were benchmarked yielding best performance on PubMed BERT with language model masking (LMM) mechanism on our methodology. This illustrates the effectiveness of our proposed strategy.作者: geriatrician 時(shí)間: 2025-3-28 03:30
Network Design Through Graph Neural Networks: Identifying Challenges and?Improving Performancet (a) edits the highest scoring edges and (b) re-embeds the edited graph to refresh gradients, leading to less biased edge choices. We empirically study ORE through a set of proposed design tasks, each with an external validation method, demonstrating that ORE improves upon previous methods by up to 50%.作者: Obsessed 時(shí)間: 2025-3-28 08:44 作者: prolate 時(shí)間: 2025-3-28 12:40 作者: Urologist 時(shí)間: 2025-3-28 17:52 作者: agonist 時(shí)間: 2025-3-28 20:44
Masking Language Model Mechanism with Event-Driven Knowledge Graphs for Temporal Relations Extractiorelations corpus for predicting BEFORE, AFTER and OVERLAP links with event graph for correct set of relations. Comparison with various Biomedical-BERT embedding types were benchmarked yielding best performance on PubMed BERT with language model masking (LMM) mechanism on our methodology. This illustrates the effectiveness of our proposed strategy.作者: LVAD360 時(shí)間: 2025-3-28 23:54
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: arthroplasty 時(shí)間: 2025-3-29 05:36
Sparse Graph Neural Networks with?Scikit-Networkleveraging sparse matrices for both graph structures and features. Our contribution enhances GNNs efficiency without requiring access to significant computational resources, unifies graph analysis algorithms and GNNs in the same framework, and prioritises user-friendliness.作者: Minutes 時(shí)間: 2025-3-29 08:42
Conference proceedings 2024k embedding and network geometry; community structure, network dynamics; diffusion, epidemics and spreading processes; machine learning and graph neural networks as well as all the main network applications, including social and political networks; networks in finance and economics; biological networks and technological networks.作者: Expand 時(shí)間: 2025-3-29 12:08 作者: 不能平靜 時(shí)間: 2025-3-29 18:17 作者: 小說(shuō) 時(shí)間: 2025-3-29 21:09 作者: 小溪 時(shí)間: 2025-3-30 00:02 作者: 使害羞 時(shí)間: 2025-3-30 06:39 作者: 不安 時(shí)間: 2025-3-30 12:07 作者: 噱頭 時(shí)間: 2025-3-30 14:34
,N?herungsl?sungen für Eigenwertprobleme,ties in healthcare access, discrimination, and economic challenges faced by these communities. The paper highlights the value of network analysis in interpreting smaller datasets and calls for further collaborations and research using the freely available survey data and analysis materials.作者: 門(mén)窗的側(cè)柱 時(shí)間: 2025-3-30 18:38 作者: 希望 時(shí)間: 2025-3-30 23:15
TimeGNN: Temporal Dynamic Graph Learning for?Time Series Forecastingd that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance.作者: nuclear-tests 時(shí)間: 2025-3-31 01:14
Stochastic Degree Sequence Model with?Edge Constraints (SDSM-EC) for?Backbone Extractionel (SDSM) that allows the null model to include edge constraints (EC) such as prohibited edges. We demonstrate the new SDSM-EC in toy data and empirical data on young children’s’ play interactions, illustrating how it correctly omits noisy edges from the backbone.作者: Schlemms-Canal 時(shí)間: 2025-3-31 05:22 作者: 留戀 時(shí)間: 2025-3-31 09:50
