Graph positive-unlabeled (GPU) learning aims to learn binary classifiers from only positive and unlabeled (PU) nodes. The state-of-the-art methods rely on provided class prior probabilistic and their performance lags far behind the fully labeled counterparts. To bridge the gap, we propose Bootstrap Latent Prototypes (BLP), a framework that consists of a graph representation learning module and a two-step strategy algorithm. The learning module bootstraps previous versions of node representations to serve as targets and learns enhanced representations by predicting the latent prototypes respectively for the P set and each individual in the U set. It eliminates the requirement for a class prior, while capturing positive similarity information, as well as the low-level semantic similarity and uniformity information, thereby producing closely aligned and discriminated representations for positive nodes. The algorithm module utilizes the obtained representations to select reliable negative nodes and train a binary classifier with both labeled positives and selected reliable negatives. Experimental results on diverse real-life datasets demonstrate that our proposed BLP method not only outperforms state-of-the-art approaches but also surpasses fully labeled classification models in most cases. The source code is available at .

中文翻译：

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用于图正未标记学习的 Bootstrap 潜在原型


图正未标记（GPU）学习旨在仅从正未标记（PU）节点学习二元分类器。最先进的方法依赖于提供的类先验概率，其性能远远落后于完全标记的对应方法。为了弥补这一差距，我们提出了 Bootstrap Latent Prototypes (BLP)，这是一个由图表示学习模块和两步策略算法组成的框架。学习模块引导先前版本的节点表示作为目标，并通过分别预测 P 集和 U 集中每个个体的潜在原型来学习增强表示。它消除了对先验类的要求，同时捕获正相似性信息以及低级语义相似性和均匀性信息，从而为正节点生成紧密对齐和可区分的表示。算法模块利用获得的表示来选择可靠的负节点，并使用标记的正节点和选定的可靠负节点训练二元分类器。在不同的现实数据集上的实验结果表明，我们提出的 BLP 方法不仅优于最先进的方法，而且在大多数情况下也超过了完全标记的分类模型。源代码可在 处获得。