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
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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
VideoSAGE: Video Summarization with Graph Representation Learning
We propose a graph-based representation learning framework for video summarization.
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
marcob46/mpxgat
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Graph representation learning has rapidly emerged as a pivotal field of study.
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
phd-lanyu/gtc
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So, can we propose a novel framework to combine GNN and Transformer, integrating both GNN's local information aggregation and Transformer's global information modeling ability to eliminate the over-smoothing problem?
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature.
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
divelab/AIRS
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Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing.
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
In this paper, we identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the selected tasks based on their importance.
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations.
Representation learning in multiplex graphs: Where and how to fuse information?
graphml-lab-pwr/multiplex-fusion
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In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community.
Graph Mamba: Towards Learning on Graphs with State Space Models
Motivated by the recent success of State Space Models (SSMs), such as Mamba, we present Graph Mamba Networks (GMNs), a general framework for a new class of GNNs based on selective SSMs.
