2015 ALearningbasedFrameworktoHandle

• (Lin et al., 2015) ⇒ Su-Chen Lin, Shou-De Lin, and Ming-Syan Chen. (2015). “A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783392

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+A+Learning-based+Framework+to+Handle+Multi-round+Multi-party+Influence+Maximization+on+Social+Networks
• http://dl.acm.org/citation.cfm?id=2783258.2783392&preflayout=flat#citedby

Quotes

Author Keywords

• Economics; influence maximization; reinforcement learning; social network

Abstract

Considering nowadays companies providing similar products or services compete with each other for resources and customers, this work proposes a learning-based framework to tackle the multi-round competitive influence maximization problem on a social network. We propose a data-driven model leveraging the concept of meta-learning to maximize the expected influence in the long run. Our model considers not only the network information but also the opponent's strategy while making a decision. It maximizes the total influence in the end of the process instead of myopically pursuing short term gain. We propose solutions for scenarios when the opponent's strategy is known or unknown and available or unavailable for training. We also show how an effective framework can be trained without manually labeled data, and conduct several experiments to verify the effectiveness of the whole process.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 ALearningbasedFrameworktoHandle	Ming-Syan Chen Shou-De Lin Su-Chen Lin			A Learning-based Framework to Handle Multi-round Multi-party Influence Maximization on Social Networks				10.1145/2783258.2783392		2015