Abstract:
Link prediction (LP) in networks predicts future interactions between elements and is a crucial machine-learning tool in genomics, social networks, marketing, and e-commerce recommender systems. Many LP techniques have been developed in the past, but most only consider static network structures and rarely include information flow. LP researches exploiting dynamic streams like information diffusion. Information diffusion allows nodes to receive information outside their social circles, which can influence new links. We analyze LP effects using susceptible-infected-recovered and independent cascade diffusion approaches. Thus, LP’s progressive-diffusion (PD) method is based on nodes’ propagation dynamics. The stochastic discrete-time rumor model uses each node’s propagation dynamics. It supports parallel and distributed processing and has low memory and processing footprints. Finally, we introduce an evaluation metric for LP methods that considers information diffusion capacity and accuracy. Experimental results on several benchmarks show that the proposed method outperforms the prior art in both criteria.
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