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 726–750 of 982 papers

TitleStatusHype
Heterogeneous Temporal Hypergraph Neural Network—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Heterophily-Aware Graph Attention Network—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
Open Domain Question Answering Using Early Fusion of Knowledge Bases and TextCode0
Universal Graph Transformer Self-Attention NetworksCode0
Cell Attention NetworksCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Heterogeneous Deep Graph InfomaxCode0
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation LearningCode0
Neural Causal Graph Collaborative FilteringCode0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
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Benchmark Results

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