Seeds Buffering for Information Spreading Processes

Abstract

Abstract. Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.

Effective Influence Spreading in Temporal Networks With Sequential Seeding

“…In another study, the ability of buffering not used seeds was analysed with seeds collected in buffer during no seeding stages. Buffer was released as soon as process stops [35]. The effect of frequency of re-computations on final coverage was also analysed.…”

“…The landscape of research on seeding approaches. The boxes in blue are the areas that are actively studied, i.e., single stage seeding in static networks ([8]- [18]); single stage seeding in temporal networks ( [19]- [22]), and sequential seeding in static networks ([27]- [30], [32], [35]- [39]) and the one in green is explored in this work, namely sequential seeding in temporal networks. a ''traditional'' single stage seeding for the same conditions [32].…”

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

“…The work on diffusion of innovation has been started by sociologist [1,20] and currently being explored by different field of studies including computer science for last few decades. Since the pioneering work of [5,21] on viral marketing, different diffusion models [1,2,3,4] and algorithms to find out the influencing individuals [3,5,6,7,9,8,10,11,12,13,14,15,16,17] have been developed. It also includes interacting spreading processes in multilayer social networks [22,23].…”

“…In this context, one question arises: 'is understanding persons' willingness to adopt from external a valuable factor for predicting the future content sharing pattern in the whole network?' Information diffusion has been forefront of the research for quite sometime [1,2,3,4] with the objective of finding the influential nodes [3,5,6,7,8,9,10,11,12,13,14,15,16,17]. Most of the previous work, with very few exceptions [4,18], only considered peer-influence in information diffusion.…”