Results 11 to 20 of about 4,082,283 (306)
Graph Neural Networks for Graph Drawing [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has paved the road to the massive usage of Gradient ...
Matteo Tiezzi +8 more
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Hyperbolic graph neural networks
CoRR, 2019 Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise.
Nickel, Maximilian +2 more
core +4 more sources
IEEE Transactions on Neural Networks and Learning Systems, 2023 Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph ...
Wu, Min +6 more
core +7 more sources
Bounded graph clustering with graph neural networks
Journal of Physics: ComplexityIn community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible values is computationally infeasible.
Kibidi Neocosmos +2 more
doaj +4 more sources
Learning Graph Matching with Graph Neural Networks
Graph matching aims at evaluating the dissimilarity of two graphs by defining a constrained correspondence between their nodes and edges. Error-tolerant graph matching, for instance, introduces the concept of a cost for penalizing structural differences ...
Dobler, Kalvin, Riesen, Kaspar
core +2 more sources
Binarized graph neural network [PDF]
World Wide Web, 2021 Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based ...
Hanchen Wang 0001 +6 more
openaire +2 more sources
Curvature graph neural network [PDF]
Information Sciences, 2022 Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism.
Haifeng Li 0007 +5 more
openaire +2 more sources
Prototype-based Interpretable Graph Neural Networks [PDF]
, 2022 Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks.
Biagio La Rosa +2 more
core +1 more source
Network In Graph Neural Network
CoRR, 2021 Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this regard, various strategies have been proposed in the past to improve the expressiveness of GNNs.
Xiang Song 0003 +4 more
openaire +3 more sources
Explainable Graph Neural Networks for Organic Cages [PDF]
, 2021 The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science.
Qi, Yuan +2 more
core +2 more sources