Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
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Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
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
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
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
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
Deep learning on graphs has become a popular research topic with many applications.
In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises.