Results 11 to 20 of about 6,963,782 (253)
Roy-lab/graph-representation-learning: v1.3
, 2023 Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algorithms for detecting modules in molecular networks"
zsong96wisc, Sushmita Roy
core +1 more source
Roy-lab/graph-representation-learning: v1.1
, 2023 Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algorithms for detecting modules in molecular networks"
zsong96wisc, Sushmita Roy
core +1 more source
Graph Tree Networks: a graph representation learning framework
, 2023 Fang, XiaoGraph Neural Networks (GNNs) have been successfully applied in many areas to solve real-world problems. Among various architectures of GNNs, the class of spatial-based convolutional GNNs (Conv-GNNs) has gained particular attention due to its ...
Wu, Nan
core +1 more source
Learning Graph Representations With Maximal Cliques. [PDF]
, 2021 Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although ...
Zare, H +9 more
core +1 more source
Graph-boosted active learning for multi-source entity resolution
, 2021 Supervised entity resolution methods rely on labeled record pairs for learning matching patterns between two or more data sources. Active learning minimizes the labeling effort by selecting informative pairs for labeling.
Bizer, Christian +3 more
core +1 more source
Online learning over graphs [PDF]
, 2005 We apply classic online learning techniques similar to the perceptron algorithm to the problem of learning a function defined on a graph. The benefit of our approach includes simple algorithms and performance guarantees that we naturally interpret in ...
Mark Herbster +5 more
core +1 more source
Graph Algorithm Animation with Grrr [PDF]
, 2000 We discuss geometric positioning, highlighting of visited nodes and user defined highlighting that form the algorithm animation facilities in the Grrr graph rewriting programming language. The main purpose of animation was initially for the debugging and
Peter J. Rodgers +3 more
core +1 more source
Robust Graph Neural Networks via Ensemble Learning
Mathematics, 2022 Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive ...
Qi Lin +6 more
doaj +1 more source
Remote Sensing, 2022 Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task.
Shu Tian +9 more
doaj +1 more source
Boosting Graph Contrastive Learning via Adaptive Sampling
, 2023 Contrastive learning (CL) is a prominent technique for self-supervised representation learning, which aims to contrast semantically similar (i.e., positive) and dissimilar (i.e., negative) pairs of examples under different augmented views.
Chen Gong +13 more
core +1 more source