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 241–250 of 982 papers

TitleStatusHype
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Graph Transformers without Positional Encodings—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Product Manifold Representations for Learning on Biological PathwaysCode0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
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Benchmark Results

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