Graph Representation

DataFunTalk

May 1, 2022 · Artificial Intelligence

Graph Deep Learning for Natural Language Processing: Methods, Models, and the Graph4NLP Library

This talk introduces graph deep learning techniques for natural language processing, covering the motivation for graph representations, traditional graph-based NLP methods, fundamentals of graph neural networks, static and dynamic graph construction, representation learning, and showcases the open‑source Graph4NLP Python library with example applications.

Graph Neural NetworksGraph RepresentationGraph4NLP

0 likes · 16 min read

Graph Deep Learning for Natural Language Processing: Methods, Models, and the Graph4NLP Library

DataFunSummit

Jan 8, 2022 · Artificial Intelligence

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve graph neural network performance without requiring task labels.

Graph Neural NetworksGraph Representationcontrastive learning

0 likes · 15 min read

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

DataFunTalk

Oct 19, 2021 · Artificial Intelligence

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

This article reviews recent research on graph self‑supervised learning, focusing on contrastive learning fundamentals, the SimCLR‑style framework, representative models such as GRACE and its adaptive augmentation extension GCA, experimental evaluations, and future directions for graph contrastive methods.

GCAGRACEGraph Neural Networks

0 likes · 16 min read

Graph Contrastive Learning: Foundations, Methods, and Recent Advances (GRACE & GCA)

DataFunSummit

Oct 19, 2021 · Artificial Intelligence

Deep Graph Contrastive Learning: GRACE and GCA

This article reviews recent advances in graph contrastive learning, introducing foundational concepts, the SimCLR framework, and representative models such as GRACE and its adaptive augmentation variant GCA, followed by experimental results, analysis, and future research directions.

GCAGRACEGraph Neural Networks

0 likes · 16 min read

Deep Graph Contrastive Learning: GRACE and GCA

DataFunTalk

Jul 7, 2021 · Artificial Intelligence

Robust Graph Representation Learning via Neural Sparsification

NeuralSparse is a supervised graph sparsification framework that removes task-irrelevant edges to improve GNN generalization, combining a sparsification network with downstream GNN training, and demonstrates superior performance across multiple graph benchmarks compared to random edge dropping and other sparsification methods.

Edge PruningGraph Neural NetworksGraph Representation

0 likes · 8 min read

Robust Graph Representation Learning via Neural Sparsification