Results 11 to 20 of about 229,000 (311)
Graph Representation Learning [PDF]
ESANN 2023 proceesdings, 2023 In a broad range of real-world machine learning applications, representing examples as graphs is crucial to avoid a loss of information. Due to this in the last few years, the definition of machine learning methods, particularly neural networks, for ...
Bacciu, Davide +13 more
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Roy-lab/graph-representation-learning: v1.1 [PDF]
, 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: v1.3 [PDF]
, 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
Learning to Learn Graph Topologies
CoRR, 2021 Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the problem can be formulated as a regularised convex optimisation over a positive semidefinite cone and solved by ...
Pu, X +4 more
openaire +4 more sources
Graph Tree Networks: a graph representation learning framework [PDF]
, 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
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Learning causality with graphs
AI Magazine, 2022 AbstractRecent years have witnessed a rocketing growth of machine learning methods on graph data, especially those powered by effective neural networks. Despite their success in different real‐world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, but lack consideration of causality. Causal inference can
Jing Ma 0002, Jundong Li
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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 ...
Hassani, Kaveh, Khasahmadi, Amir Hosein
openaire +4 more sources
Graph-boosted active learning for multi-source entity resolution [PDF]
, 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
Boosting Graph Contrastive Learning via Adaptive Sampling [PDF]
, 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
SAGES: Scalable Attributed Graph Embedding With Sampling for Unsupervised Learning [PDF]
, 2023 Unsupervised graph embedding method generates node embeddings to preserve structural and content features in a graph without human labeling burden. However, most unsupervised graph representation learning methods suffer issues like poor scalability or ...
Wang, Jialin +5 more
core +1 more source