Optimizing Link Prediction With Graph-Aware Sampling and Feature-Rich Past Graphs
Year: 2026
Type: article
Abstract: Link prediction is a foundational task in social network analysis that focuses on predicting potential or absent links between entities based on the existing network structure. Despite numerous algorithmic advances, the impact of graph density and nonedge (negative sample) selection strategies on the predictive performance of machine learning (ML) models remains underexplored. This study presents a systematic empirical investigation into these factors by constructing past graphs of varying densities; sparse, moderate, and dense, from present graphs through strategic edge removal. Negative samples are generated using three methods: Random sampling, small shortest path length (Small-SPL), and large shortest path length (Large-SPL). Ten similarity-based features are extracted, and seven ML models are evaluated using Facebook 1K (FB1K) and 4K datasets. Results demonstrate that both graph density and nonedge selection critically influence prediction outcomes. The decision tree (DT) classifier achieved the highest accuracy of 99.92% for dense graphs using the Large-SPL nonedge selection strategy. It also consistently outperformed other models across moderate and sparse graphs, achieving accuracies of 98.56% and 87.41%, respectively. These findings demonstrate the effectiveness of dense historical graphs and appropriate nonedge sampling strategies for improving link prediction accuracy. Large-SPL selection improves class separability and reduces bias, while sparse graphs lead to lower recall due to limited structural information. Ensemble models and DTs consistently perform well across conditions, whereas Naïve Bayes (NB) and SVM are more sensitive to sparsity and feature overlap. The findings highlight the importance of thoughtful dataset construction and validate the scalability of the proposed framework for robust link forecast.
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7204881320