50 papers with code ·
Graphs

Subtask of
Representation Learning

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.

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We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

Ranked #2 on Graph Classification on REDDIT-B

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

In this survey, we provide a comprehensive review on knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

GRAPH REPRESENTATION LEARNING KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING RELATIONAL REASONING

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

Ranked #1 on Graph Classification on REDDIT-B

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.

Ranked #1 on Node Classification on Wikipedia

GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.

Ranked #1 on Heterogeneous Node Classification on DBLP (PACT) 14k

GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

Ranked #1 on Graph Classification on REDDIT-MULTI-12K

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

Capturing such evolution is key to predicting the properties of unseen networks.

We examine two fundamental tasks associated with graph representation learning: link prediction and node classification.

Ranked #1 on Link Prediction on Citeseer (Accuracy metric)

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification.

Ranked #23 on Node Classification on Pubmed

GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.

GRAPH REPRESENTATION LEARNING NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDINGS