Results 21 to 30 of about 6,572,416 (223)
Cluster-driven Graph Federated Learning over Multiple Domains [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021 Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients).
Debora Caldarola +5 more
semanticscholar +1 more source
IEEE Access, 2021 Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. However, some GNNs cannot make good use of positive information brought by nodes which are far away from each central node for ...
Kehao Wang +7 more
doaj +1 more source
Local Graph Point Attention Network in Point Cloud Segmentation
IEEE Access, 2023 Exploiting global factors and embedding them directly into local graphs in point clouds are challenging due to dense points and irregular structure. To accomplish this goal, we propose a novel end-to-end trainable graph attention network that extracts ...
Anh-Thuan Tran +3 more
doaj +1 more source
Centrality-constrained graph embedding [PDF]
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 Submitted to ICASSP May ...
Baingana, Brian, Giannakis, Georgios B.
openaire +2 more sources
Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks
Machine Learning and Knowledge Extraction, 2022 Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications.
Youfa Liu, Guo Chen
doaj +1 more source
Graph Properties of the Adult Drosophila Central Brain
bioRxiv, 2020 The recent Drosophila central brain connectome offers the possibility of analyzing the graph properties of the fly brain. Crucially, this connectome is dense, meaning all nodes and links are represented, within the limits of experimental error.
Louis K. Scheffer
semanticscholar +1 more source
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits [PDF]
Journal of Chemical Information and Modeling, 2020 The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints ...
Mikolaj Sacha +4 more
semanticscholar +1 more source
Graph Prototypical Networks for Few-shot Learning on Attributed Networks [PDF]
International Conference on Information and Knowledge Management, 2020 Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery.
Kaize Ding +5 more
semanticscholar +1 more source
Regularizing graph centrality computations [PDF]
Journal of Parallel and Distributed Computing, 2015 Centrality metrics such as betweenness and closeness have been used to identify important nodes in a network. However, it takes days to months on a high-end workstation to compute the centrality of today's networks. The main reasons are the size and the irregular structure of these networks.
Ahmet Erdem Sarıyüce +3 more
openaire +1 more source
Zero-shot Node Classification with Decomposed Graph Prototype Network [PDF]
Knowledge Discovery and Data Mining, 2021 Node classification is a central task in graph data analysis. Scarce or even no labeled data of emerging classes is a big challenge for existing methods.
Zheng Wang +3 more
semanticscholar +1 more source