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Robust Graph Neural Networks using Weighted Graph Laplacian
Graph neural network (GNN) is achieving remarkable performances in a variety
of application domains. However, GNN is vulnerable to noise and adversarial
attacks in input data. Making GNN robust against noises and adversarial attacks
is an important problem. The existing defense methods for GNNs are
computationally demanding and are not scalable. In this paper, we propose a
generic framework for robustifying GNN known as Weighted Laplacian GNN
(RWL-GNN). The method combines Weighted Graph Laplacian learning with the GNN
implementation. The proposed method benefits from the positive
semi-definiteness property of Laplacian matrix, feature smoothness, and latent
features via formulating a unified optimization framework, which ensures the
adversarial/noisy edges are discarded and connections in the graph are
appropriately weighted. For demonstration, the experiments are conducted with
Graph convolutional neural network(GCNN) architecture, however, the proposed
framework is easily amenable to any existing GNN architecture. The simulation
results with benchmark dataset establish the efficacy of the proposed method,
both in accuracy and computational efficiency. Code can be accessed at
https://github.com/Bharat-Runwal/RWL-GNN.Comment: Accepted at IEEE International Conference on Signal Processing and
Communications (SPCOM), 202
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