FYP Project Updates
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Summary of Graph Attention Networks
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò and Y. Bengio. Graph Attention Networks. ICLR 2018.
- This paper introduces Graph Attention Networks (GATs), a novel neural network architecture based on masked self-attention layers for graph-structured data.
Summary of Residual Gated Graph ConvNets
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X. Bresson and T. Laurent. Residual Gated Graph ConvNets. arXiv preprint arXiv:1711.07553.
- Focus of this paper was to compare between RNN and ConvNet architectures on two basic graph problems: Subgraph Matching and Semi-Supervised Clustering, in the variable graph setting.
Final Year Project: Semi-Supervised Clustering with Graph Neural Networks
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As part of my coursework at NTU, I will be doing research on the topic of Semi-Supervised Clustering with Graph Neural Networks under Prof Xavier Bresson.