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Inductive Representation Learning On Temporal Graphs by azogiby is a document available to read on EtoBox.

What is Inductive Representation Learning On Temporal Graphs about?

The paper presents a novel approach for inductive representation learning on temporal graphs using a Temporal Graph Attention (TGAT) layer that captures both temporal and topological features. The proposed method utilizes a self-attention mechanism combined with a functional time encoding technique, allowing for efficient aggregation of neighborhood features and the ability to infer embeddings for new nodes as the graph evolves. Experimental results demonstrate that TGAT outperforms existing state-of-the-ar

Author
azogiby
Language
EN