Graph Representation Learning

368 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.

Source: SIGN: Scalable Inception Graph Neural Networks

Libraries

Use these libraries to find Graph Representation Learning models and implementations

GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation

phd-lanyu/gtc 22 Mar 2024

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?

0
22 Mar 2024

Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction

junhua/stged 20 Mar 2024

Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature.

0
20 Mar 2024

Complete and Efficient Graph Transformers for Crystal Material Property Prediction

divelab/AIRS 18 Mar 2024

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.

374
18 Mar 2024

Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis

wonderlandxd/wikg 12 Mar 2024

Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing.

9
12 Mar 2024

Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks

tianyufan0504/was 3 Mar 2024

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.

7
03 Mar 2024

Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions

mengyingjiang/hmgrl 28 Feb 2024

Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations.

1
28 Feb 2024

Representation learning in multiplex graphs: Where and how to fuse information?

graphml-lab-pwr/multiplex-fusion 27 Feb 2024

In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community.

0
27 Feb 2024

Graph Mamba: Towards Learning on Graphs with State Space Models

graphmamba/gmn 13 Feb 2024

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.

44
13 Feb 2024

TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning

facebookresearch/taser-tgnn 8 Feb 2024

Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.

8
08 Feb 2024

Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching

nus-hpc-ai-lab/ctrl 7 Feb 2024

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns.

4
07 Feb 2024