基于协作推断的正例未标注图学习算法

Positive unlabeled graph learning based on collective inference

针对现有正例未标注图学习方法仅提取节点表征信息、独立推断节点类别的问题,提出了一种基于协作推断分类算法,利用节点之间关联信息来帮助推断未标注节点的标签。首先,采用个性化网页排位算法计算每个节点与全体已知正例节点的关联度。其次,采用一个图神经网络学习节点表征信息,与正例关联度联合构造一个局部分类器,预测未标注节点标签;采用另一个图神经网络获取局部节点标签之间依赖关系,与正例关联度联合构造一个关系分类器,协作更新未标注节点标签。然后,借鉴马尔可夫图神经网络方法交替迭代地训练两者,形成多跳步节点标签之间的协作推断;并且,为有效利用正例与未标注节点训练分类器,提出了混合非负无偏风险评估函数。最后,选择两者中任意一个,预测未标注节点的类别。在真实数据集上的实验结果表明,无论是识别单类别正例还是识别多类别合成正例,所述算法均表现出比其他正例未标注学习方法更佳效果,且对正例先验概率误差表现出更好的鲁棒性。

Most existing positive-unlabeled(PU)graph learning methods exact only node representations to infer node labels independently.This paper proposed a collective inference based method that exploited the correlations among nodes to assist in classification of unlabeled nodes.Firstly,it used the personalized PageRank algorithm to approximate correlation degrees between each node and observed positive nodes as a whole(positive correlation degree,PCD for short).Then,it built a local classifier to predict labels of unknown nodes by combining PCD with node representations that were captured via a graph neural network(GNN),and constructed a relational classifier to collectively update labels of unknown nodes by combining PCD with local label dependency of nodes that were exacted via another GNN.Furtherly,it exploited the Markov GNN(GMNN)framework to train these two classifiers,alternately and iteratively,to form a multi-hop collective inference procedure.Besides,it proposed a mixed non-negative unbiased risk estimator for the two classifiers to estimate empirical loss with only positive and unlabeled nodes.Finally,either of them could predict labels of unknown nodes.Experimental results on real-life datasets show that the proposed method remarkably outperforms the state-of-the-art approaches in identifying both single-class target concept and multiple-classes-merged target concept,and performs quite robust against to error of the prior of positive nodes.