222 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 neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION

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 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)

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

Ranked #15 on 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.

Ranked #2 on Node Classification on Flickr

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

Ranked #12 on Link Prediction on WN18

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

Ranked #3 on Malware Detection on Android Malware Dataset

LINK PREDICTION MULTI-LABEL CLASSIFICATION NODE CLASSIFICATION REPRESENTATION LEARNING

Therefore, how to ﬁnd a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

Ranked #2 on Graph Classification on BP-fMRI-97

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

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.

Ranked #2 on Knowledge Graphs on FB15k

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING