Seeds Buffering for Information Spreading Processes

摘要

Seeding strategies for influence maximization in social networks have beenstudied for more than a decade. They have mainly relied on the activation ofall resources (seeds) simultaneously in the beginning; yet, it has been shownthat sequential seeding strategies are commonly better. This research focuseson studying sequential seeding with buffering, which is an extension to basicsequential seeding concept. The proposed method avoids choosing nodes that willbe activated through the natural diffusion process, which is leading to betteruse of the budget for activating seed nodes in the social influence process.This approach was compared with sequential seeding without buffering and singlestage seeding. The results on both real and artificial social networks confirmthat the buffer-based consecutive seeding is a good trade-off between the finalcoverage and the time to reach it. It performs significantly better than itsrivals for a fixed budget. The gain is obtained by dynamic rankings and theability to detect network areas with nodes that are not yet activated and havehigh potential of activating their neighbours.