Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
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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
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
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
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
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