Effective Influence Spreading in Temporal Networks with Sequential Seeding

The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence. To tackle this and similar challenges, more than a decade ago, researchers started to investigate the influence maximisation problem. The challenge is to find the best set of initially activated seed nodes in order to maximise the influence spread in networks. In typical approach we will activate all seeds in single stage, at the beginning of the process, while in this work we introduce and evaluate a new approach for seeds activation in temporal networks based on sequential seeding. Instead of activating all nodes at the same time, this method distributes the activations of seeds, leading to higher ranges of influence spread. The results of experiments performed using real and randomised networks demonstrate that the proposed method outperforms single stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal networks are an adequate choice for modelling dynamic processes, the results of this work can be interpreted as encouraging to apply temporal sequential seeding for real world cases, especially knowing that more sophisticated seed selection strategies can be implemented by using the seed activation strategy introduced in this work.Comment: 11 pages, 10 figures, reproductory code availabl

Understanding Moderators of Peer Influence for Engineering Viral Marketing Seeding Simulations and Strategies

Seeding as an emerging viral marketing strategy requires a better understanding on how various contextual factors that embedded in social networks affect peer influence and product diffusion. Realistic simulations for seeding need to incorporate empirical insights about the complexities (various moderators) and dynamics (temporal changes) of peer influence by analyzing real-world data. We analyze the impacts of peer influence moderators in a large-scale phone call network of 0.48 million customers with 364 million calls and 3.9 million video-on-demand purchases, to design empirical models and engineer data-driven simulations of product diffusion, as well as developing and evaluating seeding strategies. We intend to contribute to existing research by 1) enriching the theoretical and empirical understanding of peer influence moderators for stakeholders, 2) combining econometric models and analyses with data-driven simulations towards a complex system approach for devising and evaluating effective seeding strategies in different scenarios