Community-Aware Quantum Link Prediction in Complex Networks with Inter-Community Information

Link prediction is a key issue in network science, trying to predict missing and future links in a network. Finding these missing links is very useful in many applications, like suggesting friends on social media, reducing the economic cost of protein functional modular mining, and giving better shopping recommendations. Traditional approaches based on classical graph theory often struggle with complex networks. Recently, the Quantum Link Prediction (QLP) framework was proposed as a novel method that employs quantum simulation to capture non-local relationships among network elements. However, QLP faces two main challenges that limit its applicability to complete graphs. First, it has a high computational cost. Second, its performance decreases as the size of the graph increases. In this paper, we present an enhanced QLP framework. The graph is partitioned into communities, and QLP is applied within each community. Inter-community links are also incorporated during link prediction to preserve structural information across communities. Experimental results show that the proposed method reduces computational time while maintaining or improving the prediction accuracy of the original QLP framework.

کلیدواژه ها:

Quantum Link Prediction ، community detection ، Complex networks ، Link Prediction