Graph Representation Learning

123 papers with code • 1 benchmarks • 4 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

Latest papers without code

Graphine: A Dataset for Graph-aware Terminology Definition Generation

no code yet • 9 Sep 2021

Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation.

Graph Representation Learning Text Generation

Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs

no code yet • 8 Sep 2021

To address these challenges, we propose a novel Graph Multi-View Prototypical (Graph-MVP) framework to extract node embeddings on multiplex graphs.

Contrastive Learning Graph Representation Learning +1

Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning

no code yet • 6 Sep 2021

In a different GRL approach, spectral methods based on graph filtering have emerged addressing over smoothing; however, up to now, they employ traditional neural networks that cannot efficiently exploit the structure of graph data.

Graph Representation Learning

ETA Prediction with Graph Neural Networks in Google Maps

no code yet • 25 Aug 2021

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.

Graph Representation Learning

Graph Trend Networks for Recommendations

no code yet • 12 Aug 2021

Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives.

Graph Representation Learning Recommendation Systems

HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations

no code yet • 12 Aug 2021

A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.

Knowledge Graph Embedding Link Prediction

Localized Graph Collaborative Filtering

no code yet • 10 Aug 2021

These methods often make recommendations based on the learned user and item embeddings.

Graph Representation Learning Recommendation Systems

DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation

no code yet • 7 Aug 2021

The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements.

Graph Representation Learning

Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets

no code yet • 3 Aug 2021

In this paper, we propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets.

Graph Representation Learning

Graph Representation Learning on Tissue-Specific Multi-Omics

no code yet • 25 Jul 2021

Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks.

Graph Embedding Graph Representation Learning +1