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 751–800 of 982 papers

TitleStatusHype
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation LearningCode1
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
Graph Pooling via Coarsened Graph InfomaxCode0
UniGNN: a Unified Framework for Graph and Hypergraph Neural NetworksCode1
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Graph Representation Learning in Biomedicine—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
New Benchmarks for Learning on Non-Homophilous GraphsCode1
Sub-GMN: The Neural Subgraph Matching Network Model—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Adversarial Graph DisentanglementCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Towards a Unified Framework for Fair and Stable Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Calibrating and Improving Graph Contrastive LearningCode0
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Predicting Patient Outcomes with Graph Representation LearningCode1
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Learning Latent Topology for Graph Matching—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
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Benchmark Results

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