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 231–240 of 982 papers

TitleStatusHype
Bridging the Gap between Community and Node Representations: Graph Embedding via Community DetectionCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
Heterogeneous Deep Graph InfomaxCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
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Benchmark Results

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