Results 271 to 280 of about 6,652,811 (287)

Leveraging Standardization in Graph Learning

European Signal Processing Conference
Standardization of the variables is a staple scaling tool that can be beneficial in most statistical learning tasks. In the context of graph learning, we propose to take further advantage of this preprocessing, as it yields an additional implicit ...
Thu Ha Phi, Alexandre Hippert-Ferrer, Florent Bouchard, Arnaud Breloy   +3 more
semanticscholar   +1 more source

Graph transfer learning

Knowledge and Information Systems, 2021
Andrey Gritsenko, Kimia Shayestehfard, Yuan Guo, Armin Moharrer, Jennifer Dy, Stratis Ioannidis   +5 more
openaire   +1 more source

Attributed Graph Force Learning

IEEE Transactions on Neural Networks and Learning Systems
In numerous network analysis tasks, feature representation plays an imperative role. Due to the intrinsic nature of networks being discrete, enormous challenges are imposed on their effective usage. There has been a significant amount of attention on network feature learning in recent times that has the potential of mapping discrete features into a ...
Ke Sun, Feng Xia, Jiaying Liu, Bo Xu, Vidya Saikrishna, Charu C. Aggarwal   +5 more
openaire   +2 more sources

Reverse Graph Learning for Graph Neural Network

IEEE Transactions on Neural Networks and Learning Systems
Graph neural networks (GNNs) conduct feature learning by taking into account the local structure preservation of the data to produce discriminative features, but need to address the following issues, i.e., 1) the initial graph containing faulty and missing edges often affect feature learning and 2) most GNN methods suffer from the issue of out-of ...
Liang Peng, Rongyao Hu, Fei Kong, Jiangzhang Gan, Yujie Mo, Xiaoshuang Shi, Xiaofeng Zhu   +6 more
openaire   +2 more sources

Graph explicit pooling for graph-level representation learning

Neural Networks
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discarding the remaining to construct coarsened graph representations.
Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Wenbin Hu, Bo Du   +7 more
openaire   +2 more sources

Learning Graph Matching

2007 IEEE 11th International Conference on Computer Vision, 2007
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs.
Tiberio S. Caetano, null Li Cheng, Quoc V. Le, Alex J. Smola   +3 more
openaire   +1 more source

AGCL: Adaptive Graph Contrastive Learning for graph representation learning

Neurocomputing, 2023
Jiajun Yu, Adele Lu Jia
openaire   +1 more source

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 in the matching.
Kalvin Dobler, Kaspar Riesen
openaire   +1 more source

Graph Learning

2022
Setareh Rafatirad, Houman Homayoun, Zhiqian Chen, Sai Manoj Pudukotai Dinakarrao   +3 more
openaire   +1 more source

Learning Graph

2021
openaire   +1 more source