Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
This addresses the challenge of accurately predicting missing links in graphs for applications like social network friend recommendations, though it appears incremental as it builds on existing PU learning techniques.
The paper tackles the problem of link prediction in edge-incomplete graphs, where missing links are common due to practical limitations, and proposes PULL, a method based on positive-unlabeled learning that treats observed edges as positives and unconnected pairs as unlabeled, achieving consistent outperformance over baselines in experiments on five real-world datasets.
Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding friends in a social network. Addressing the problem is crucial for various tasks, including recommending friends in social networks and finding references in citation networks. However, previous approaches rely heavily on the given edge-incomplete (observed) graph, making it challenging to consider the missing (unobserved) links during training. In this paper, we propose PULL (PU-Learning-based Link predictor), an accurate link prediction method based on the positive-unlabeled (PU) learning. PULL treats the observed edges in the training graph as positive examples, and the unconnected node pairs as unlabeled ones. PULL effectively prevents the link predictor from overfitting to the observed graph by proposing latent variables for every edge, and leveraging the expected graph structure with respect to the variables. Extensive experiments on five real-world datasets show that PULL consistently outperforms the baselines for predicting links in edge-incomplete graphs.