Link Prediction Based on Stochastic Information Diffusion

Author(s)

Vega-Oliveros, Didier A. ^{[1, 2]} ; Zhao, Liang ^[3] ; Rocha, Anderson ^[1] ; Berton, Lilian ^[4]

Affiliation

^[1] Univ Estadual Campinas, RECOD Lab, Inst Comp, BR-13083852 Campinas, SP - Brazil

^[2] Indiana Univ, Luddy Sch Informat Comp & Engn, Ctr Complex Networks & Syst Res, Bloomington, IN 47408 - USA

^[3] Univ Sao Paulo, Dept Comp & Math, Fac Philosophy Sci & Letters Ribeirao Preto FFCLR, BR-14040901 Ribeirao Preto, SP - Brazil

^[4] Univ Fed Sao Paulo, Inst Sci & Technol, BR-12247014 Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS; FEB 2021.
• Web of Science Citations: 1

Abstract

Link prediction (LP) in networks aims at determining future interactions among elements; it is a critical machine-learning tool in different domains, ranging from genomics to social networks to marketing, especially in e-commerce recommender systems. Although many LP techniques have been developed in the prior art, most of them consider only static structures of the underlying networks, rarely incorporating the network's information flow. Exploiting the impact of dynamic streams, such as information diffusion, is still an open research topic for LP. Information diffusion allows nodes to receive information beyond their social circles, which, in turn, can influence the creation of new links. In this work, we analyze the LP effects through two diffusion approaches, susceptible-infected-recovered and independent cascade. As a result, we propose the progressive-diffusion (PD) method for LP based on nodes' propagation dynamics. The proposed model leverages a stochastic discrete-time rumor model centered on each node's propagation dynamics. It presents low-memory and low-processing footprints and is amenable to parallel and distributed processing implementation. Finally, we also introduce an evaluation metric for LP methods considering both the information diffusion capacity and the LP accuracy. Experimental results on a series of benchmarks attest to the proposed method's effectiveness compared with the prior art in both criteria. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 18/24260-5 - Spatiotemporal Data Analytics based on Complex Networks
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 16/23698-1 - Dynamical Processes in Complex Network based on Machine Learning
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/01722-3 - Semi-supervised learning via complex networks: network construction, selection and propagation of labels and applications
• Grantee: Lilian Berton
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/26283-5 - Learning visual clues of the passage of time
• Grantee: Didier Augusto Vega Oliveros
• Support Opportunities: Scholarships in Brazil - Post-Doctoral