47 papers with code ·
Graphs

Subtask of
Representation Learning

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

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facebookresearch/PyTorch-BigGraph •

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

NeurIPS 2017 • facebookresearch/poincare-embeddings •

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

ICLR 2018 • PetarV-/GAT •

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.

#3 best model for Document Classification on Cora

DOCUMENT CLASSIFICATION GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.

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.

#5 best model for Node Classification on Wikipedia

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

benedekrozemberczki/graph2vec •

•Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

SOTA for Graph Classification on NCI109

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

#2 best model for Node Classification on Wikipedia

We study the problem of learning to reason in large scale knowledge graphs (KGs).

KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS

NeurIPS 2017 • Hanjun-Dai/graph_comb_opt

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

ICLR 2018 • snap-stanford/GraphRNN •

Deep learning on graphs has become a popular research topic with many applications.