Adaptive Influence Maximization in Dynamic Social Networks

Abstract

Abstract-For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. Unfortunately, as shown in the experiments, in such cases the state-of-art seeding strategies are pessimistic as they fails to trace the dynamic changes in a social network. In this paper, we study the strategies selecting seed users in an adaptive manner. We first formally model the Dynamic Independent Cascade model and introduce the concept of adaptive seeding strategy. Then based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm an efficient heuristic algorithm is provided in order to meet practical requirements. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other standard methods.Index Terms-Social network influence, adaptive seeding strategy, stochastic submodular maximization.

A survey on influence maximization in a social network

“…However, this is not a practical assumption, as most of the social networks are time varying. Recent studies on SIM Problem started considering temporal nature of the social network [135], [136]. As this has just started, there is a lot of scope to work in TSS Problem in time-varying social networks.…”

The evaluation and application of node influence in dynamic networks based on evolving communities

“…4 Based on the DIC model, adaptive seeding strategies to select a target set were designed, which select seeds in a dynamic manner instead of initially. 4 Based on the DIC model, adaptive seeding strategies to select a target set were designed, which select seeds in a dynamic manner instead of initially.…”

“…To capture the dynamic aspects of real social networks, Tong et al developed a dynamic independent cascade (DIC) model under the probabilistic scenario. 4 Based on the DIC model, adaptive seeding strategies to select a target set were designed, which select seeds in a dynamic manner instead of initially. These strategies have proven to be effective for practical requirements.…”

“…[1][2][3][4] In a cellular network where mobile worms exist, if we can patch an optimal set of influential phones, the propagation of worms can be effectively prevented. [1][2][3][4] In a cellular network where mobile worms exist, if we can patch an optimal set of influential phones, the propagation of worms can be effectively prevented.…”

No Time to Observe: Adaptive Influence Maximization with Partial Feedback

“…Leveraging these nice properties, they propose an elegant greedy algorithm with constant approximation ratio. Since then, considerable work (Chen et al 2013, Leskovec et al 2007, Cohen et al 2014, Chen et al 2010, 2009, Tang et al 2011, Tang and Yuan 2016, Tong et al 2016, Yuan and Tang 2017 has been devoted to this topic and its variants.…”