Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Ranked #2 on Node Classification on Wiki-Vote

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.

Ranked #1 on Link Prediction on YouTube (Macro F1 metric)

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

Ranked #2 on Node Classification on Wikipedia

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

Ranked #5 on Node Classification on Wikipedia

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

We show popular embedding models are indeed uncalibrated.

CALIBRATION FOR LINK PREDICTION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.

Ranked #1 on Lexical Entailment on HyperLex

In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.

COMMUNITY DETECTION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MINING NODE CLASSIFICATION

Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs.