Name
Title | ||
|---|---|---|
CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing |
Abstract | ||
|---|---|---|
Influence maximization problem with applications to viral marketing has gained much attention. Underlying influence diffusion models affect influence maximizing nodes because they focus on difference aspect of influence diffusion. Nevertheless, existing diffusion models overlook two important aspects of real-world marketing - continuous trials and time restriction. This paper proposes a new realistic influence diffusion model called Continously activated and Time-restricted IC (CT-IC) model which generalizes the IC model by embedding the above two aspects. We first prove that CT-IC model satisfies two crucial properties - monotonicity and submodularity. We then provide an efficient method for calculating exact influence spread when a social network is restricted to a directed tree and a simple path. Finally, we propose a scalable algorithm for influence maximization under CT-IC model called CT-IPA. Our experiments show that CT-IC model provides seeds of higher influence spread than IC model and CT-IPA is four orders of magnitude faster than the greedy algorithm while providing similar influence spread to the greedy algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDM.2012.40 | ICDM |
Keywords | Field | DocType |
ct-ic model,similar influence spread,monotonicity property,ic model,trees (mathematics),social network,underlying influence diffusion model,continuously activated,new realistic influence diffusion,continuous trials,influence diffusion model,time-restricted independent cascade model,marketing data processing,submodularity property,greedy algorithm,influence maximization,influence maximization problem,viral marketing,social networking (online),influence diffusion,viral marketing social networks,time restriction,directed tree,continuously activated cascade model,exact influence spread,higher influence spread,ct-ic | Data mining,Monotonic function,Viral marketing,Embedding,Path (graph theory),Computer science,Greedy algorithm,Cascade,Artificial intelligence,Maximization,Machine learning,Diffusion (business) | Conference |
ISSN | ISBN | Citations |
1550-4786 | 978-1-4673-4649-8 | 13 |
PageRank | References | Authors |
0.60 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Won Yeol Lee | 1 | 3310 | 219.72 |
| Jin-ha Kim | 2 | 329 | 18.78 |
| Hwanjo Yu | 3 | 1715 | 114.02 |