圖神經網絡展示了它們在建模圖結構方面的強大能力，並在各種領域任務中達到了卓越的性能。然而，在真實世界中的圖往往存在雜訊，並且帶有標籤的節點數量非常少，這會大幅的降低圖神經網路模型在下游任務的表現，使得圖神經網路難以運用在真實情境中。過去的許多研究假定輸入的節點特徵是乾淨的，並以此去學習對有雜訊的圖結構做重建，但輸入的節點特徵也帶有雜訊時，就不適合使用這些方法。因此，本研究提出一個穩健性的圖神經網路模型，可以對有雜訊的圖結構與節點特徵做修正，並在有標籤的節點數量非常少的情形下，仍然能為每個節點做出正確的預測結果。首先，我們使用拉普拉斯平滑化，使得原本給定的具有雜訊的節點特徵在經過平滑化以後，可以較接近沒有雜訊的節點特徵經過平滑化後的特徵分布，並使相鄰節點的距離會更接近。然後，我們會利用平滑過的節點特徵，輸入到一個同質性增強編碼器，進一步使得原本具有高相似性的的節點會學到更相似的特徵表示，反之則會學到越不相似的特徵表示。接著，我們會將學到的節點特徵表示輸入到一個用於執行鏈接預測的編碼器，依據每個節點特徵表示計算彼此的相似度作為節點對之間的邊權重，以此來降低或消除被視為雜訊的邊並形成更多鏈接，使訊息能更有效的傳遞，最後，我們將學習到的節點特徵表示與圖結構輸入到圖神經網路分類器，執行節點分類任務，並加入一項正則化，使得原本沒有標籤的節點可以直接被計算到目標函數中。在實驗階段，我們使用四個常見的學術引用網路資料集進行節點分類任務、模型穩健性分析、消融分析、超參數分析。實驗結果證明我們的方法在輸入帶有雜訊的圖結構與節點特徵時，相較於過去的方法，可以得到較高的準確率，表示我們提出的模型對於圖結構以及節點特徵的雜訊具有穩健性，並在標籤稀疏性的狀況下也能為每個節點做出正確的預測。

Graph Neural Networks (GNNs) have exhibited their capability on modeling graph, and reached epic performance on various domains. However, graphs in the real world often contain structure/feature noise and have few labeled nodes, which significantly diminishes the performance of GNNs in downstream tasks, making it difficult to apply GNNs in real-world scenarios. Previous studies assume that the input node features are clean and use this assumption to learn how to reconstruct graph. Nevertheless, when the node features are also noisy, these methods are no longer suitable. Therefore, we propose a Adversarial Robust GNN (ARGNN) that can correct noisy graph and node features, and make accurate predictions for each node with limited labeled nodes. First, we use Laplacian smoothing filter to denoise the given noisy node features and makes the smoothed node features more closer to the smoothed clean node features, while enhance the homophily for neighboring nodes. Then, we utilize the smoothed node feature as input to a homophily-augmented encoder, which learns more similar node embeddings for nodes with originally similar features and more dissimilar node embeddings for nodes with dissimilar features. After obtaining the learned node embedding matrix, we feed it into a link predictor to learn different edge weights based on the pairwise feature similarity of each node pair to reduce/remove the noisy edges and create more connections for more effectively message-passing. Finally, we input the learned node embeddings and the graph structure to a GNN classifier for node classification task, and introduce a regularization term which makes the unlabeled nodes can be directly computed into the objective function. In experiment phase, we conduct node classification task, model robustness analysis, ablation study, and hyperparameter analysis on four citation network dataset. The experiment results demonstrate that our method achieves higher accuracy compared to state-of-the-art approaches when the input graph and node features contain noise, meaning that our proposed framework is robustness to the structural noise and feature noise, and can make correct prediction for each node with limited labeled nodes.

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