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|Title:||Graph attention networks and approximate personalized propagation of neural prediction models for unsupervised graph representation learning||Authors:||Bharadwaja, Tanay||Keywords:||Engineering::Computer science and engineering||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Bharadwaja, T. (2022). Graph attention networks and approximate personalized propagation of neural prediction models for unsupervised graph representation learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156556||Project:||SCSE21-0377||Abstract:||Recent years have brought progress in the graph machine learning space, with the unsupervised graph representation learning field gaining traction due to the immense resources required to label graph data. A leading approach in the field, Deep Graph InfoMax, has been shown to provide good performance in training Graph Convolutional Networks (GCNs) for the task in an unsupervised manner suing mutual information. In this paper, we proposed the novel approach of using Graph Attention Networks (GATs) and Approximate Personalized Propagation of Neural Prediction (APPNP) models trained with the Deep Graph InfoMax training method. We tested the transductively trained models on three challenging graph benchmarks and used a small training sample along with a Logistic Regression classifier to evaluate the quality of the representations generated. GAT models showed good performance and were able to attain a similar accuracy to GCN-based approaches. However, APPNP models were not able to learn well from the Deep Graph InfoMax training method, with lacklustre performance. The success of the GAT models solidifies the theory behind the training method, and we suggest that more developments on GAT variants suited to Deep Graph InfoMax be done to bring better learning through mutual information. On the other hand, the APPNP models require further improvements to be trained with mutual information for arbitrary graphs. Increased computing power to tackle larger benchmarks would also prove to be useful for the graph representation learning task.||URI:||https://hdl.handle.net/10356/156556||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
Updated on May 19, 2022
Updated on May 19, 2022
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