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
刊名	KNOWLEDGE AND INFORMATION SYSTEMS
	2021-03-19
页码	24
关键词	Influence function learning Influence maximization Information cascades Social network
ISSN号	0219-1377
DOI	10.1007/s10115-021-01556-6
英文摘要	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.
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
语种	英语
出版者	SPRINGER LONDON LTD
WOS记录号	WOS:000630644700001
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16880]
专题	中国科学院计算技术研究所
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.