Graph Representation Learning
376 papers with code • 1 benchmarks • 6 datasets
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.
Benchmarks
These leaderboards are used to track progress in Graph Representation Learning
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Libraries
Use these libraries to find Graph Representation Learning models and implementations
Reddit
IMDB-BINARY
COMA
WikiGraphs
Myket Android Application Install
Latest papers
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
facebookresearch/taser-tgnn
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Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns.
No Need to Look Back: An Efficient and Scalable Approach for Temporal Network Representation Learning
graph-com/nlb
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This strategy is implemented using a GPU-executable size-constrained hash table for each node, recording down-sampled recent interactions, which enables rapid response to queries with minimal inference latency.
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
tonyauyeung/local2gae2global
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For analysing real-world networks, graph representation learning is a popular tool.
Graph Domain Adaptation: Challenges, Progress and Prospects
To the best of our knowledge, this paper is the first survey for graph domain adaptation.
Graph Contrastive Learning with Cohesive Subgraph Awareness
However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process.
Product Manifold Representations for Learning on Biological Pathways
mcneela/mixed-curvature-pathways
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Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively in the biological domain, particularly with respect to biological pathway graphs.
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
wyy-code/TFP
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However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings.
Graph Representation Learning for Contention and Interference Management in Wireless Networks
Additionally, we present an architecture that uses the online-measured throughput and path losses to fine-tune the decisions in response to changes in user populations and their locations.
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning
In light of the issues above, we propose the problem of \emph{Motif-aware Riemannian Graph Representation Learning}, seeking a numerically stable encoder to capture motif regularity in a diverse-curvature manifold without labels.
