Many existing Graph Neural Networks (GNN) methods assume that labels are reliable and sufficient, which may not be the case in real-world scenarios. This paper addresses one such problem of Partial Label Learning (PLL) on graph-structured data. In the PLL for graphs, each node is represented by a candidate set of labels, where only one is true while the others are inaccurate. Despite advancements with PLL in tabular and vision domains, the graph-structured data still needs to be explored. In this work, we first define PLL for graphs. Subsequently, we propose a new PLD-Graph algorithm for PLL in homogeneous graphs with scarce labels. We utilize graph augmentation to reduce the effects of inexact labels and provide additional supervision from unlabeled nodes. Progressive label disambiguation is performed based on the model's ability to predict correct classes. Furthermore, an additional loss estimates the label corruption matrix to capture associations between correct and incorrect labels. We show the effectiveness of the proposed algorithm on multiple graph datasets, with two types of noise and varying levels of ambiguous labels. Overall, the proposed PLD-Graph algorithm outperforms state-of-the-art PLL methods.
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Progressive Label Disambiguation for Partial Label Learning in Homogeneous Graphs
OpenAlex · Text and Document Classification Technologies · 2024
Abstract
Many existing Graph Neural Networks (GNN) methods assume that labels are reliable and sufficient, which may not be the case in real-world scenarios. This paper addresses one such problem of Partial Label Learning (PLL) on graph-structured data. In the PLL for graphs, each node is represented by a candidate set of labels, where only one is true while the others are inaccurate. Despite advancements with PLL in tabular and vision domains, the graph-structured data still needs to be explored. In this work, we first define PLL for graphs. Subsequently, we propose a new PLD-Graph algorithm for PLL in homogeneous graphs with scarce labels. We utilize graph augmentation to reduce the effects of inexact labels and provide additional supervision from unlabeled nodes. Progressive label disambiguation is performed based on the model's ability to predict correct classes. Furthermore, an additional loss estimates the label corruption matrix to capture associations between correct and incorrect labels. We show the effectiveness of the proposed algorithm on multiple graph datasets, with two types of noise and varying levels of ambiguous labels. Overall, the proposed PLD-Graph algorithm outperforms state-of-the-art PLL methods.