IRIE: Scalable and Robust Influence Maximization in Social Networks

Details IRIE: Scalable and Robust Influence Maximization in Social Networks IRIE: Scalable and Robust Influence Maximization in Social Networks

2011-11-21

arxiv.org/abs/1111.4795v3

Computer Science

Social and Information Networks

Scientific paper

Influence maximization is the problem of selecting top $k$ seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates a new message passing based influence ranking (IR), and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Through extensive experiments, we demonstrate that IRIE matches the influence coverage of other algorithms while scales much better than all other algorithms. Moreover IRIE is more robust and stable than other algorithms both in running time and memory usage for various density of networks and cascade size. It runs up to two orders of magnitude faster than other state-of-the-art algorithms such as PMIA for large networks with tens of millions of nodes and edges, while using only a fraction of memory comparing with PMIA.

Affiliated with

Also associated with

No associations

Rating

IRIE: Scalable and Robust Influence Maximization in Social Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with IRIE: Scalable and Robust Influence Maximization in Social Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and IRIE: Scalable and Robust Influence Maximization in Social Networks will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-376333