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 with code

Graph-based Incident Aggregation for Large-Scale Online Service Systems

opspai/grlia 27 Aug 2021

The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud.

Graph Representation Learning

27 Aug 2021

Jointly Learnable Data Augmentations for Self-Supervised GNNs

zekarias-tilahun/graph-surgeon 23 Aug 2021

Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs.

Data Augmentation Graph Representation Learning +2

23 Aug 2021

Self-supervised Consensus Representation Learning for Attributed Graph

topgunlcs98/scrl 10 Aug 2021

Self-supervised loss is designed to maximize the agreement of the embeddings of the same node in the topology graph and the feature graph.

Graph Representation Learning Node Classification +1

10 Aug 2021

Hyperparameter-free and Explainable Whole Graph Embedding

HW-HaoWang/DHC-E 4 Aug 2021

In practice, graph embedding (graph representation learning) attempts to learn a lower-dimensional representation vector for each node or the whole graph while maintaining the most basic information of graph.

Graph Classification Graph Embedding +2

04 Aug 2021

CCGL: Contrastive Cascade Graph Learning

Xovee/ccgl 27 Jul 2021

Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.

Data Augmentation Graph Learning +2

27 Jul 2021

Local2Global: Scaling global representation learning on graphs via local training

LJeub/Local2Global_embedding 26 Jul 2021

Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.

Graph Reconstruction Graph Representation Learning +2

26 Jul 2021

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

deepmind/deepmind-research 20 Jul 2021

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.

Conditional Text Generation Graph Generation +2

20 Jul 2021

Large-scale graph representation learning with very deep GNNs and self-supervision

deepmind/deepmind-research 20 Jul 2021

In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.

Denoising Graph Representation Learning

20 Jul 2021

Graph Representation Learning for Road Type Classification

zahrag/GAIN 16 Jul 2021

We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks.

Classification Graph Attention +1

16 Jul 2021

A Deep Latent Space Model for Graph Representation Learning

upperr/DLSM 22 Jun 2021

Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture.

Community Detection Graph Representation Learning +1

22 Jun 2021