Influence analysis: A survey of the state-of-the-art

Abstract

Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference. 201 202 MENG HAN AND YINGSHU LIachieved. Even so, prior to the Internet, quantitative data of social networks were scanty and the further influence analysis in social networks was in the slow-lane. In 2007, Nicholas et al. [47] published their years of research results based on the historical data from the spreading of obesity over 32 years. From the same research filed, David et al. [11] proposed another idea about the spread of obesity in social networks based on the simulations which further considered the group effect in obesity spreading. Both works tried to explore how the influence diffusion in social networks affects obesity. In their model, individuals' influence over each other rely on food intake and physical activities [70]. Since other models consider obesity as a "contagious" phenomenon that can be caught if most social contracts are deemed obese, the interaction of social networks with environmental factors could not be explored. It was not accounted for in the general model where the social networks were proposed as a means to mitigate the obesity epidemic. Many other research results have been obtained recently, such as smoking behavior [48][52], happiness [68], and loneliness [25][117] which spread along a social network over time.At an American high school, Salath et al.[207] obtained high-resolution data of close proximity interactions during a typical day, and their work helps with the reconstruction of a social network for infectious disease transmission by using wireless sensor network technology [246,98]. Through simulations, they showed that targeted immunization using the contact-network data is much more effective than random immunization. Stehle et al. [216] report a similar result like Salath [207] in a French primary school. The team headed by Stehle also provided several public-health implications of infectious diseases by collecting a period of history data from their experiments. By analyzing the real experiments in two ...

A Hurst-based Diffusion Model using Time Series Characteristics for Influence Maximization in Social Networks

“…The widespread popularity of OSNs is owing to their applicability in numerous areas like finance, bio-informatics, politics, healthcare, social awareness, etc. Using OSNs for diffusing (spreading) information is a part of many real-life activities such as viral marketing [1], [2], [3], [4], [5], online recommendation systems [4], [3], [2], online advertising [4], [6], [5], influential blogger identification [7], [5], healthcare communities [7], [3] , and political and social awareness campaigns.…”

A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks

“…The widespread popularity of OSNs is owing to their applicability in numerous areas like finance, bio‐informatics, politics, healthcare, social awareness, etc. Using OSNs for diffusing (spreading) information is a part of many real‐life activities such as viral marketing, online recommendation systems, online advertising, influential blogger identification, healthcare communities, and political and social awareness campaigns (Del Ser et al, 2022; Domingos & Richardson, 2001; Han & Li, 2018; Li, Zhang, & Huang, 2018; Peng et al, 2017; Peng et al, 2018; Sun & Tang, 2011).…”

“…communities, and political and social awareness campaigns (Del Ser et al, 2022;Domingos & Richardson, 2001;Han & Li, 2018;Li, Zhang, & Huang, 2018;Peng et al, 2017;Peng et al, 2018;Sun & Tang, 2011).…”

On the Submodularity of Diffusion Models: Equivalent Conditions and Applications

“…To reduce the cost of calculation in such problems, [1] proposed that: if we take advantage of the submodularity of diffusion model and the theorems in [2], then the greedy algorithm (during which K nodes that maximize the marginal propagation increment are selected sequentially) can result in a propagation range which is no smaller than 1 − 1 e of the theoretical optimal propagation range. Studies hitherto [3][4] [5] utilize other graph features or extra data structures to boost the calculation and have derived diversified and fruitful results. However, the proof of the submodularity of an arbitrary diffusion model remains challenging and inspiring, [1] proved the submodularity of two specific diffusion models, namely independent cascade model (IC) and linear threshold model (LT), but what about a general diffusion model?…”