Learning diffusion model-free and efficient influence function for influence maximization from information cascades

doi:10.1007/s10115-021-01556-6

	Learning diffusion model-free and efficient influence function for influence maximization from information cascades
	Cao, Qi 1,2; Shen, Huawei 1,2; Gao, Jinhua 1; Cheng, Xueqi 1
	2021-03-19
发表期刊	KNOWLEDGE AND INFORMATION SYSTEMS
ISSN	0219-1377
页码	24
摘要	When considering the problem of influence maximization from information cascades, one essential component is influence estimation. Traditional approaches for influence estimation generally follow a two-stage framework, i.e., learn a hypothetical diffusion model from information cascades and then calculate the influence spread according to the learned diffusion model via Monte Carlo simulation or heuristic approximation. The effectiveness of these approaches heavily relies on the correctness of the diffusion model, suffering from the problem of model misspecification. Meanwhile, these approaches are inefficient when influence estimation is conducted via lots of Monte Carlo simulations. In this paper, without assuming a diffusion model a priori, we directly learn a monotone and submodular influence function from information cascades. Once the influence function is obtained, greedy algorithm is applied to efficiently solve influence maximization. Experimental results on both synthetic and real-world datasets show the effectiveness and efficiency of the learned influence function for both influence estimation and influence maximization tasks.
关键词	Influence function learning Influence maximization Information cascades Social network
DOI	10.1007/s10115-021-01556-6
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[91746301] ; National Natural Science Foundation of China[62041207] ; National Natural Science Foundation of China[61472400] ; National Natural Science Foundation of China[61425016] ; K.C. Wong Education Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000630644700001
出版者	SPRINGER LONDON LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16881
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shen, Huawei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cao, Qi,Shen, Huawei,Gao, Jinhua,et al. Learning diffusion model-free and efficient influence function for influence maximization from information cascades[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2021:24.
APA	Cao, Qi,Shen, Huawei,Gao, Jinhua,&Cheng, Xueqi.(2021).Learning diffusion model-free and efficient influence function for influence maximization from information cascades.KNOWLEDGE AND INFORMATION SYSTEMS,24.
MLA	Cao, Qi,et al."Learning diffusion model-free and efficient influence function for influence maximization from information cascades".KNOWLEDGE AND INFORMATION SYSTEMS (2021):24.