66 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.

Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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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.

#2 best model for Skeleton Based Action Recognition on J-HMBD Early Action

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

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

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

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.

#9 best model for Graph Classification on NCI109

ICLR 2018 • JiaxuanYou/graph-generation •

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

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.