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

Showing 741750 of 982 papers

TitleStatusHype
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)0.47Unverified