174 papers with code ·
Graphs

Link prediction is a task to estimate the probability of links between nodes in a graph.

( Image credit: Inductive Representation Learning on Large Graphs )

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

We consider learning representations of entities and relations in KBs using the neural-embedding approach.

#12 best model for Link Prediction on WN18

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.

#5 best model for Node Classification on Cora Full-supervised

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

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.

#10 best model for Link Prediction on WN18

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.

SOTA for Knowledge Graphs on FB15k

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.

We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces.

#12 best model for Link Prediction on WN18RR

We consider matrix completion for recommender systems from the point of view of link prediction on graphs.

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

SOTA for Node Classification on AIFB

GRAPH CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION