Conference proceeding
Active Learning of Model Parameters for Influence Maximization
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I, v 6911(1), pp 280-295
01 Jan 2011
Abstract
Previous research efforts on the influence maximization problem assume that the network model parameters are known beforehand. However, this is rarely true in real world networks. This paper deals with the situation when the network information diffusion parameters are unknown. To this end, we firstly examine the parameter sensitivity of a popular diffusion model in influence maximization, i.e., the linear threshold model, to motivate the necessity of learning the unknown model parameters. Experiments show that the influence maximization problem is sensitive to the model parameters under the linear threshold model. In the sequel, we formally define the problem of finding the model parameters for influence maximization as an active learning problem under the linear threshold model. We then propose a weighted sampling algorithm to solve this active learning problem. Extensive experimental evaluations on five popular network datasets demonstrate that the proposed weighted sampling algorithm outperforms pure random sampling in terms of both model accuracy and the proposed objective function.
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Details
- Title
- Active Learning of Model Parameters for Influence Maximization
- Creators
- Tianyu Cao - University of VermontXindong Wu - University of VermontTony Xiaohua Hu - Drexel UniversitySong Wang - University of Vermont
- Contributors
- D Gunopulos (Editor)T Hofmann (Editor)D Malerba (Editor)M Vazirgiannis (Editor)
- Publication Details
- MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I, v 6911(1), pp 280-295
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 16
- Grant note
- CCF-0905337 / US National Science Foundation (NSF); National Science Foundation (NSF) CCF 0905291 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000316544000028
- Scopus ID
- 2-s2.0-80052424082
- Other Identifier
- 991019170607504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods