Graph Representation Learning
384 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.
Libraries
Use these libraries to find Graph Representation Learning models and implementationsLatest papers with no code
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.
Investigating Similarities Across Decentralized Financial (DeFi) Services
To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label.
Exploring Task Unification in Graph Representation Learning via Generative Approach
Specifically, GA^2E proposes to use the subgraph as the meta-structure, which remains consistent across all graph tasks (ranging from node-, edge-, and graph-level to transfer learning) and all stages (both during training and inference).
Graph Partial Label Learning with Potential Cause Discovering
PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels.
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning
In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial.
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Robust Graph Structure Learning under Heterophily
In this regard, we propose a novel robust graph structure learning method to achieve a high-quality graph from heterophilic data for downstream tasks.
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework.
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph.