Abstract:
Link prediction aims to identify missing or future connections between entities
of a complex system, when modeled as a network. This research problem has attracted
significant attention due to its relevance in numerous fields. In this work, we
propose a novel link prediction approach that integrates various network centrality
metrics in order to predict the likelihood of future connections between entities of a
system. Namely, we consider weighted betweenness, closeness and Katz centralities,
in addition to the Resource Allocation and Adamic-Adar indices. We also use a
genetic algorithm to optimize the weights of these metrics, reflecting their contributions
to link prediction. We tested our method on several real-world benchmark
networks. Our experimental results show that the proposed approach outperforms
various state-of-the-art link prediction approaches, highlighting its effectiveness and
validity.
