Graph Representation Learning
375 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 with no code
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains.
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
Intuitively, we can expect the generated embeddings become smooth asymptotically layerwisely, that is each layer of graph convolution generates a smoothed version of embeddings as compared to that generated by the previous layer.
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
To address this issue, we propose a neighbour-level message interaction information encoding method for improving graph representation learning.
Graph Neural Networks for Binary Programming
This paper investigates a link between Graph Neural Networks (GNNs) and Binary Programming (BP) problems, laying the groundwork for GNNs to approximate solutions for these computationally challenging problems.
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs.
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Inspired by the recent advances in graph representation learning, we propose to re-sketch the missing modalities problem as a problem of missing graph node features to apply the state-of-the-art feature propagation algorithm eventually.
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference.
ChebMixer: Efficient Graph Representation Learning with MLP Mixer
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems.
