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 881–890 of 982 papers

TitleStatusHype
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
Deep Representation Learning For Multimodal Brain Networks—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers—0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
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Benchmark Results

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