Semi-Supervised Learning in Graph Neural Networks: A Spectral Filtering Approach

Abstract

In this thesis, we investigated the general framework of Graph Neural Network for node and graph classification tasks. We studied the phenomenon of over smoothing and conducted experiments to validate them. We propose a novel spectral-based Legendre Filter based on the Legendre polynomial to learn node features on graphstructured data. We also described various aggregation schemes that can be employed with the Legendre Filter to further improve information aggregation. Furthermore, we proposed a novel algorithm that changes graph topology based on some heuristics to improve overall classification accuracy. For Semi-Supervised learning, we demonstrated that our proposed method performs better than GCN and Chebyshev Filter on Citation Network datasets. Our proposed model outperformed GAT on Citeseer and PubMed. For the full-supervised learning task, we showed that our method outperforms all three baselines; GCN, GAT, and Chebyshev Filter on Citation Network and WebKB datasets. We further showed that our method outperforms deep GNN models like GCNII, JKNet on the WebKB dataset.