Influence maximization algorithm based on directed acyclic graph in heterogeneous information networks

doi:10.11772/j.issn.1001-9081.2021020369

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 895-903.DOI: 10.11772/j.issn.1001-9081.2021020369

• Data science and technology • Previous Articles Next Articles

Influence maximization algorithm based on directed acyclic graph in heterogeneous information networks

Qingqing WU¹, Lihua ZHOU¹(), Xuanyi CUN¹, Guowang DU¹, Yiting JIANG²

^1.School of information Science & Engineering，Yunnan University，Kunming Yunnan 650000，China
^2.School of information，Yunnan Normal University，Kunming Yunnan 650000，China

Received:2021-03-11 Revised:2021-05-11 Accepted:2021-05-13 Online:2021-05-27 Published:2022-03-10
Contact: Lihua ZHOU
About author:WU Qingqing， born in 1995， M. S. candidate. Her research interests include data mining， heterogeneous information network， information diffusion.
CUN Xuanyi， born in 1995， M. S. candidate. His research interests include data mining， heterogeneous information network，information diffusion.
DU Guowang， born in 1994， Ph. D. candidate. His research interests include multi-perspective learning.
JIANG Yiting， born in 1983， M. S.， lecturer. Her research interests include data mining.
Supported by:
National Natural Science Foundation of China(62062066);Key Project of Yunnan Province Applied Basic Research Program in 2022

异质信息网络中基于有向无环图的影响力最大化算法

吴晴晴¹, 周丽华¹(), 寸轩懿¹, 杜国王¹, 姜懿庭²

^1.云南大学信息学院，昆明 650000
^2.云南师范大学信息学院，昆明 650000

通讯作者: 周丽华
作者简介:吴晴晴（1995—），女，安徽亳州人，硕士研究生，主要研究方向：数据挖掘、异质信息网络、信息扩散
寸轩懿（1995—），男，云南腾冲人，硕士研究生，主要研究方向：数据挖掘、异质信息网络、信息扩散
杜国王（1994—），男，河南洛阳人，博士研究生，主要研究方向：多视角学习
姜懿庭（1983—），女，云南昆明人，讲师，硕士，主要研究方向：数据挖掘。
基金资助:
国家自然科学基金资助项目(62062066);2022年云南省应用基础研究计划重点项目

Abstract

Abstract:

Aiming at the problem of Influence Maximization （IM） in heterogeneous information networks， an Influence Maximization algorithm （DAGIM） based on Directed Acyclic Graph （DAG） was proposed. Firstly， the influence of nodes was measured based on the DAG structure， and then the marginal gain strategy was used to select the nodes with the most influence. The DAG structure has strong expressive power， which not only describes the explicit relationship between different types of nodes， but also depicts the implicit relationship between nodes， and more completely retains the heterogeneous information of the network. Experimental results on three real datasets verify that the performance of the proposed DAGIM algorithm is better than those of Degree， PageRank， Local Directed Acyclic Graph （LDAG） and Meta-Path-based Information Entropy （MPIE） algorithms.

Key words: social network, heterogeneous information network, information diffusion, influence maximization, Directed Acyclic Graph (DAG)

摘要：

针对异质信息网络中的影响力最大化（IM）问题，提出了一种基于有向无环图（DAG）的影响力最大化算法（DAGIM）。首先基于DAG结构度量节点的影响力，然后采用边际增益策略选择影响力最大的节点。DAG结构表达力强，不仅描述了不同类型节点之间的显性关系，也刻画了节点之间的隐性关系，较完整地保留了网络的异质信息。在三个真实数据集上的实验结果验证所提DAGIM的性能优于Degree、PageRank、局部有向无环图（LDAG）以及基于元路径的信息熵（MPIE）算法。

关键词: 社会网络, 异质信息网络, 信息扩散, 影响力最大化, 有向无环图

CLC Number:

TP391.9

Qingqing WU, Lihua ZHOU, Xuanyi CUN, Guowang DU, Yiting JIANG. Influence maximization algorithm based on directed acyclic graph in heterogeneous information networks[J]. Journal of Computer Applications, 2022, 42(3): 895-903.

吴晴晴, 周丽华, 寸轩懿, 杜国王, 姜懿庭. 异质信息网络中基于有向无环图的影响力最大化算法[J]. 《计算机应用》唯一官方网站, 2022, 42(3): 895-903.

Figures/Tables 9

References 33

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数据集	节点类型	节点数量	边类型	边数量
DBLP	Author（A）	7 677	P-A	28 939
	Paper（P）	14 376	P-C	14 376
	Paper（P）	20	P-T	114 624
	Term（T）	8 920
Yelp	User （U）	5 023	U-B	62 268
	Business （B）	14 284	U-U	50 532
	Category （Cat）	47	B-Cit	14 257
	City （Cit）	511	B-Cat	40 009
Amazon	User（U）	6 170	U-I	195 791
	Item（I）	2 753	I-V	5 694
	View（V）	3 857	I-C	5 508
	Category（C）	22	I-B	2 753
	Brand（B）	334

数据集	节点类型	节点数量	边类型	边数量
DBLP	Author（A）	7 677	P-A	28 939
	Paper（P）	14 376	P-C	14 376
	Paper（P）	20	P-T	114 624
	Term（T）	8 920
Yelp	User （U）	5 023	U-B	62 268
	Business （B）	14 284	U-U	50 532
	Category （Cat）	47	B-Cit	14 257
	City （Cit）	511	B-Cat	40 009
Amazon	User（U）	6 170	U-I	195 791
	Item（I）	2 753	I-V	5 694
	View（V）	3 857	I-C	5 508
	Category（C）	22	I-B	2 753
	Brand（B）	334

数据集	种子节点数K	运行耗时/s
数据集	种子节点数K	PageRank	Degree	MPIE	LDAG	DAGIM
DBLP	10	15.98	0.13	64 792.2	17 210.31	272.08
	20	18.13	0.35	64 795.8	17 239.42	277.66
	30	20.95	0.41	64 796.3	17 268.34	281.73
	40	22.50	0.56	64 799.0	17 302.55	287.29
	50	24.39	0.74	64 807.7	17 344.71	292.34
YELP	10	10.73	0.12	113 380.5	11 011.61	769.82
	20	11.10	0.25	113 385.2	11 038.57	774.49
	30	11.42	0.40	113 392.5	11 061.38	776.63
	40	12.03	0.52	113 399.1	11 092.07	779.72
	50	12.49	0.65	113 409.6	11 119.87	781.39
Amazon	10	5.67	0.14	53 320.3	13 310.30	3 816.98
	20	10.65	0.24	53 325.5	13 319.50	3 819.63
	30	11.23	0.29	53 328.8	13 326.81	3 823.72
	40	12.17	0.39	53 332.6	13 338.34	3 826.84
	50	12.67	0.44	53 335.1	13 343.21	3 828.74

数据集	种子节点数K	运行耗时/s
数据集	种子节点数K	PageRank	Degree	MPIE	LDAG	DAGIM
DBLP	10	15.98	0.13	64 792.2	17 210.31	272.08
	20	18.13	0.35	64 795.8	17 239.42	277.66
	30	20.95	0.41	64 796.3	17 268.34	281.73
	40	22.50	0.56	64 799.0	17 302.55	287.29
	50	24.39	0.74	64 807.7	17 344.71	292.34
YELP	10	10.73	0.12	113 380.5	11 011.61	769.82
	20	11.10	0.25	113 385.2	11 038.57	774.49
	30	11.42	0.40	113 392.5	11 061.38	776.63
	40	12.03	0.52	113 399.1	11 092.07	779.72
	50	12.49	0.65	113 409.6	11 119.87	781.39
Amazon	10	5.67	0.14	53 320.3	13 310.30	3 816.98
	20	10.65	0.24	53 325.5	13 319.50	3 819.63
	30	11.23	0.29	53 328.8	13 326.81	3 823.72
	40	12.17	0.39	53 332.6	13 338.34	3 826.84
	50	12.67	0.44	53 335.1	13 343.21	3 828.74

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Influence maximization algorithm based on directed acyclic graph in heterogeneous information networks

异质信息网络中基于有向无环图的影响力最大化算法

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