Results 21 to 30 of about 8,038,825 (297)
Advances in the Development of Representation Learning and Its Innovations against COVID-19
COVID, 2023 In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or ...
Peng Li +4 more
doaj +1 more source
Adaptive Graph Representation for Clustering
IEEE Access, 2022 Many graph construction methods for clustering cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the
Mei Chen +5 more
doaj +1 more source
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
Multi-scale contrastive siamese networks for self-supervised graph representation learning [PDF]
, 2021 Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To overcome this problem, inspired by the recent success of graph contrastive
Chen Gong +17 more
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
Dual Graph Representation Learning
CoRR, 2020 Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs.
Huiling Zhu, Xin Luo, Hankz Hankui Zhuo
openaire +2 more sources
Learning Graph Augmentations to Learn Graph Representations
CoRR, 2022 Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn ...
Kaveh Hassani, Amir Hosein Khas Ahmadi
openaire +2 more sources
Graph Representation Learning for Wireless Communications [PDF]
, 2023 Wireless networks are inherently graph-structured in which graph representation learning can be utilized to solve complex network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such ...
Rajatheva, Nandana +4 more
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
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating [PDF]
, 2022 Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks.
Liu, Y +9 more
core +1 more source