SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 151–175 of 982 papers

TitleStatusHype
LMSOC: An Approach for Socially Sensitive PretrainingCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
Pre-training Molecular Graph Representation with 3D GeometryCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
Graph Trend Filtering Networks for RecommendationsCode1
CCGL: Contrastive Cascade Graph LearningCode1
Edge Representation Learning with HypergraphsCode1
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
Evaluating Modules in Graph Contrastive LearningCode1
Self-supervised Graph-level Representation Learning with Local and Global StructureCode1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation LearningCode1
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
UniGNN: a Unified Framework for Graph and Hypergraph Neural NetworksCode1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
New Benchmarks for Learning on Non-Homophilous GraphsCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Adversarial Graph DisentanglementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified