書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications影響因子(影響力)學(xué)科排名
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications網(wǎng)絡(luò)公開度
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications被引頻次
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications被引頻次學(xué)科排名
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications年度引用
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications年度引用學(xué)科排名
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications讀者反饋
書目名稱Graph Neural Networks: Foundations, Frontiers, and Applications讀者反饋學(xué)科排名
作者: 在前面 時間: 2025-3-22 00:05 作者: 船員 時間: 2025-3-22 01:39 作者: Lasting 時間: 2025-3-22 08:14
The Expressive Power of Graph Neural Networkshniques to overcome these limitations, such as injecting random attributes, injecting deterministic distance attributes, and building higher-order GNNs. We will present the key insights of these techniques and highlight their advantages and disadvantages.作者: FOLD 時間: 2025-3-22 11:44 作者: 一大群 時間: 2025-3-22 14:27
Graph Neural Networks: Graph Transformationegories, namely node-level transformation, edge-level transformation, node-edge co-transformation, as well as other graph-involved transformations (e.g., sequenceto- graph transformation and context-to-graph transformation), which are discussed in Section 12.2 to Section 12.5, respectively. In each 作者: 一大群 時間: 2025-3-22 18:36 作者: 獨白 時間: 2025-3-22 21:44 作者: LARK 時間: 2025-3-23 01:49 作者: Harness 時間: 2025-3-23 06:23
https://doi.org/10.1007/978-3-662-36442-0hniques to overcome these limitations, such as injecting random attributes, injecting deterministic distance attributes, and building higher-order GNNs. We will present the key insights of these techniques and highlight their advantages and disadvantages.作者: 舊石器 時間: 2025-3-23 11:42 作者: 錯 時間: 2025-3-23 14:15
Mikroskopie und Chemie am Krankenbettegories, namely node-level transformation, edge-level transformation, node-edge co-transformation, as well as other graph-involved transformations (e.g., sequenceto- graph transformation and context-to-graph transformation), which are discussed in Section 12.2 to Section 12.5, respectively. In each 作者: Cupping 時間: 2025-3-23 20:31
https://doi.org/10.1007/978-3-662-36436-9raph matching problem, we provide a formal definition and discuss state-of-the-art GNN-based models for both the classic graph matching problem and the graph similarity problem, respectively. Finally, this chapter is concluded by pointing out some possible future research directions.作者: Assault 時間: 2025-3-23 23:34
Angelica Schrader,Alfred Krischhat have been proposed in the literature. We conclude by reviewing three notable applications of dynamic graph neural networks namely skeleton-based human activity recognition, traffic forecasting, and temporal knowledge graph completion.作者: Eulogy 時間: 2025-3-24 05:16
https://doi.org/10.1007/978-3-322-91181-0ntations, this chapter focuses on deep learning methods: those that are formed by the composition of multiple non-linear transformations, with the goal of resulting in more abstract and ultimately more useful representations. We summarize the representation learning techniques in different domains, 作者: 楓樹 時間: 2025-3-24 07:20 作者: 懶惰人民 時間: 2025-3-24 10:52
https://doi.org/10.1007/978-3-658-23702-8 have achieved huge successes on Euclidean data such as images, or sequence data such as text, there are many applications that are naturally or best represented with a graph structure. This gap has driven a tide in research for deep learning on graphs, among them Graph Neural Networks (GNNs) are th作者: colony 時間: 2025-3-24 17:59 作者: Isolate 時間: 2025-3-24 19:46
https://doi.org/10.1007/978-3-662-36442-0predictions. Since the universal approximation theorem by (Cybenko, 1989), many studies have proved that feed-forward neural networks can approximate any function of interest. However, these results have not been applied to graph neural networks (GNNs) due to the inductive bias imposed by additional作者: 揮舞 時間: 2025-3-25 02:52 作者: calumniate 時間: 2025-3-25 07:02 作者: 前面 時間: 2025-3-25 09:48
https://doi.org/10.1007/978-3-662-28408-7lecular property prediction, cancer classification, fraud detection, or knowledge graph reasoning. With the increasing number of GNN models deployed in scientific applications, safety-critical environments, or decision-making contexts involving humans, it is crucial to ensure their reliability. In t作者: MELON 時間: 2025-3-25 14:32
Mikroskopie und Chemie am Krankenbettpter gives an overview of GNNs for graph classification, i.e., GNNs that learn a graphlevel output. Since GNNs compute node-level representations, pooling layers, i.e., layers that learn graph-level representations from node-level representations, are crucial components for successful graph classifi作者: 個人長篇演說 時間: 2025-3-25 19:20
Mikroskopie und Chemie am Krankenbett widely used in social networks, citation networks, biological networks, recommender systems, and security, etc. Traditional link prediction methods rely on heuristic node similarity scores, latent embeddings of nodes, or explicit node features. Graph neural network (GNN), as a powerful tool for joi作者: 同來核對 時間: 2025-3-25 22:33
Mikroskopie und Chemie am Krankenbettl. Then we introduce several representative modern graph generative models that leverage deep learning techniques like graph neural networks, variational auto-encoders, deep auto-regressive models, and generative adversarial networks. At last, we conclude the chapter with a discussion on potential f作者: Coeval 時間: 2025-3-26 00:20 作者: 腐蝕 時間: 2025-3-26 06:29 作者: CERE 時間: 2025-3-26 11:35 作者: Fracture 時間: 2025-3-26 14:40 作者: Moderate 時間: 2025-3-26 19:41 作者: 假裝是你 時間: 2025-3-27 00:08 作者: Meditative 時間: 2025-3-27 03:44
Graph Representation Learningcently, a significant amount of progress has been made toward this emerging graph analysis paradigm. In this chapter, we first summarize the motivation of graph representation learning. Afterwards and primarily, we provide a comprehensive overview of a large number of graph representation learning m作者: 引水渠 時間: 2025-3-27 09:20 作者: Genistein 時間: 2025-3-27 12:28
Graph Neural Networks for Node Classificationy and applied to different domains and applications. In this chapter, we focus on a fundamental task on graphs: node classification.We will give a detailed definition of node classification and also introduce some classical approaches such as label propagation. Afterwards, we will introduce a few re作者: forestry 時間: 2025-3-27 13:48
The Expressive Power of Graph Neural Networkspredictions. Since the universal approximation theorem by (Cybenko, 1989), many studies have proved that feed-forward neural networks can approximate any function of interest. However, these results have not been applied to graph neural networks (GNNs) due to the inductive bias imposed by additional作者: 幼稚 時間: 2025-3-27 19:42 作者: 鎮(zhèn)痛劑 時間: 2025-3-27 22:29 作者: lymphoma 時間: 2025-3-28 02:57 作者: 未完成 時間: 2025-3-28 07:54 作者: Occupation 時間: 2025-3-28 12:21 作者: Ancestor 時間: 2025-3-28 18:38 作者: prosperity 時間: 2025-3-28 22:38
Graph Neural Networks: Graph Transformationget domain, which requires to learn a transformation mapping from the source to target domains. For example, it is important to study how structural connectivity influences functional connectivity in brain networks and traffic networks. It is also common to study how a protein (e.g., a network of at作者: gratify 時間: 2025-3-29 00:12 作者: 一起 時間: 2025-3-29 03:22
Graph Neural Networks: Graph Structure Learningplications such as Natural Language Processing, Computer Vision, recommender systems, drug discovery and so on. However, the great success of GNNs relies on the quality and availability of graph-structured data which can either be noisy or unavailable. The problem of graph structure learning aims to作者: PAGAN 時間: 2025-3-29 08:57
Dynamic Graph Neural Networks crucial building-block for machine learning applications; the nodes of the graph correspond to entities and the edges correspond to interactions and relations. The entities and relations may evolve; e.g., new entities may appear, entity properties may change, and new relations may be formed between作者: Banquet 時間: 2025-3-29 13:14 作者: 使無效 時間: 2025-3-29 18:52 作者: Diastole 時間: 2025-3-29 22:45 作者: 高談闊論 時間: 2025-3-30 01:22 作者: corn732 時間: 2025-3-30 05:42
Graph Representation Learningn of graph representation learning. Afterwards and primarily, we provide a comprehensive overview of a large number of graph representation learning methods in a systematic manner, covering the traditional graph representation learning, modern graph representation learning, and graph neural networks.作者: Generosity 時間: 2025-3-30 08:57 作者: ABHOR 時間: 2025-3-30 13:41 作者: 甜食 時間: 2025-3-30 19:53
https://doi.org/10.1007/978-3-642-50739-7presentative architectures of graph neural networks for node classification. We will further point out the main difficulty— the oversmoothing problem—of training deep graph neural networks and present some latest advancement along this direction such as continuous graph neural networks.作者: 周興旺 時間: 2025-3-30 21:09
Mikroskopie und Chemie am Krankenbettcation. Hence, we give a thorough overview of pooling layers. Further, we overview recent research in understanding GNN’s limitations for graph classification and progress in overcoming them. Finally, we survey some graph classification applications of GNNs and overview benchmark datasets for empirical evaluation.作者: 流眼淚 時間: 2025-3-31 01:28
https://doi.org/10.1007/978-3-642-20936-9 brief review of the recent development on HG embedding, then introduce typical methods from the perspective of shallow and deep models, especially HGNNs. Finally, it will point out future research directions for HGNNs.作者: 放牧 時間: 2025-3-31 06:34
