Preference-based mining of top-KK influential nodes in social networks

Many important applications can be generalized as the influence maximization problem, which targets finding a KK-node set in a social network that has the maximum influence. Previous work only considers that influence is propagated through the network with a uniform probability. However, because users actually have different preferences on topics, such a uniform propagation can result in inaccurate results.To solve this problem, we have designed a two-stage mining algorithm (GAUP) to mine the most influential nodes in a network on a given topic. Given a set of users’ documents labeled with topics, GAUP first computes user preferences with a latent feature model based on SVD or a model based on vector space. Then to find top-KK nodes in the second stage, GAUP adopts a greedy algorithm that is guaranteed to find a solution within 63% of the optimal. Our evaluation on the task of expert finding shows that GAUP performs better than the state-of-the-art greedy algorithm, SVD-based collaborative filtering, and HITS.

► We propose a two-stage mining algorithm (GAUP) for mining the most influential nodes.
► GAUP can find more influential domain experts than SVD-based collaborative filtering.
► GAUP performs more reliably than HITS for expert search.
► LSI-based user preferences are more effective than those of the VSM.