Information Spread and Topic Diffusion in Heterogeneous Information Networks

Soheila Molaei,Mostafa Salehi,Mahdi Jalili,Sama Babaei

doi:10.1038/s41598-018-27385-2

Abstract

Diffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges. In this manuscript, we model information diffusion in heterogeneous information networks, and use interactions of different meta-paths to predict the diffusion process. A meta-path is a path between nodes across different layers of a heterogeneous network. As its most important feature the proposed method is capable of determining the influence of all meta-paths on the diffusion process. A conditional probability is used assuming interdependent relations between the nodes to calculate the activation probability of each node. As independent cascade models, we consider linear threshold and independent cascade models. Applying the proposed method on two real heterogeneous networks reveals its effectiveness and superior performance over state-of-the-art methods.

Highlights

Information diffusion is one of the widely studied dynamical processed on networks, which has potential applications in fields
We show that Independent Cascade (IC) model has more accurate answer than Linear threshold (LT) model in properly modeling topic diffusion in heterogeneous networks
This paper studied information spread and diffusion of scientific topics in heterogeneous networks

Summary

Introduction

Information diffusion is one of the widely studied dynamical processed on networks, which has potential applications in fields. By Zhou et al.[11] In this process, a heterogeneous network connects the users, products, and words, based on which the learning process is conducted using sentiment classification. A heterogeneous network connects the users, products, and words, based on which the learning process is conducted using sentiment classification In this regard, Zhou et al.[11] proposed a co-ranking method which classifies the authors and documents separately based on random walks. Considering an epidemic threshold, Wang and Dai[21] addressed virus spreading in heterogeneous networks based on the well-known susceptible-infected-susceptible model. A heterogeneous network model was proposed for new product diffusion in two stages by Li and Jin[29]; the first stage is transition of information concerning new products to customers through advertisement, and the second stage is changing customer priorities through persuasive advertisements

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Conclusion