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 371380 of 982 papers

TitleStatusHype
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Graph-based Incident Aggregation for Large-Scale Online Service SystemsCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
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Benchmark Results

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