Abstract
BackgroundInfluence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics.MethodsIn this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch.ResultsWe explore two preprocessing algorithms with theoretical justifications.ConclusionsOur empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.Electronic supplementary materialThe online version of this article (doi:10.1186/s40649-016-0033-z) contains supplementary material, which is available to authorized users.
Highlights
Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized
The network is associated with a stochastic diffusion model characterizing the influence propagation dynamics starting from the seed nodes
We provide a theoretical justification showing that Marginal Influence Sort (MIS) can be as good as the offline greedy algorithm when nodes are fully separated by topics
Summary
Information, ideas, rumors, and innovations can be propagated to a large number of people because of the social influence between the connected peers in the network. Comparing to [17], our contributions include: (a) we include data analysis on two real-world datasets with learned influence parameters, which shows different topical influence properties and motivates our algorithm design; (b) we provide theoretical justifications to our algorithms; (c) the use of marginal influence of seeds in individual topics in MIS is novel, and is complementary to the approach in [17]; (d) even though MIS is quite simple, it achieves competitive influence spread within microseconds of online processing time rather than milliseconds needed in [17]. We define influence spread of seed set S under influence probability function p, denoted σ (S, p), as the expected number of active nodes after the diffusion process ends. This indicates that some topics are more likely to propagate than others
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