基于用户影响与兴趣的社交网信息传播模型

doi:10.11959/j.issn.1000-436x.2017264

Journal on Communications ›› 2017, Vol. 38 ›› Issue (Z2): 113-121.doi: 10.11959/j.issn.1000-436x.2017264

• Papers • Previous Articles Next Articles

Social network information diffusion model based on user’s influence and interesting

Rui WANG¹,Yong LIU¹(),Jing-hua ZHU¹,Ping XUAN¹,Jin-bao LI¹

¹ School of Computer Science and Technology,Heilongjiang University,Harbin 150080,China
² Key Laboratory of Database and Parallel Computing of Heilongjiang Province,Harbin 150080,China

Online:2017-11-01 Published:2018-06-07
Supported by:
The National Natural Science Foundation of China(6137022);The National Natural Science Foundation of China(61602159);The National Natural Science Foundation of China(61300225);The Natural Science Foundation of Heilongjiang Province(F201430);The Natural Science Foundation of Heilongjiang Province(F2015013);The Innovation Talents Project of Science and Technology Bureau of Harbin(2017RAQXJ094);The Innovation Talents Project of Science and Technology Bureau of Harbin(2015RAQXJ004);The Innovation Talents Project of Science and Technology Bureau of Harbin(2015RAXXJ004);The Fundamental Research Funds of Universities in Heilongjiang Province(HDJCCX-201608)

Abstract

Abstract:

A new non-topological information diffusion model of social network was proposed,called non-topological influence-interest diffusion model (NT-II).Representation learning was exploited to construct two hidden spaces for NT-II，called the user-influence space and the user-interest space,each user and each propagation item was mapped into a vector in space.The model predicted the probability of a user receiving a propagated item,considering not only the degree of influence from other users,but also the user's preference for propagated item.The experimental results show that the model can simulate the propagation process and predict the propagation results more accurately.

Key words: diffusion model, representation learning, user-influence space, user-interest space

CLC Number:

TP311

Rui WANG,Yong LIU,Jing-hua ZHU,Ping XUAN,Jin-bao LI. Social network information diffusion model based on user’s influence and interesting[J]. Journal on Communications, 2017, 38(Z2): 113-121.

Figures/Tables 5

References 15

[1]	海沫, 郭庆 . 在线社交网络信息传播模型研究[J]. 小型微型计算机系统, 2016,37(8): 1672-1679.
	HAI M , GUO Q . Research on online social network information transmission model[J]. Small Microcomputer System, 2016,37(8): 1672-1679.
[2]	GOMEZ RODRIGUEZ M , LESKOVEC J , LKOPF B . Structure and dynamics of information pathways in online media[C]// ACM International Conference on Web Search and Data Mining. ACM, 2013: 23-32.
[3]	YANG J , LESKOVEC J . Modeling information diffusion in implicit networks[C]// ICDM 2010,the IEEE International Conference on Data Mining. 2011: 599-608.
[4]	BOURIGAULT S , LAMPRIER S , GALLINARI P . Representation learning for information diffusion through social networks:an embedded cascade model[C]// ACM International Conference on Web Search and Data Mining. 2016: 573-582.
[5]	FENG S , LI X , ZENG Y ,et al. Personalized ranking metric embedding for next new POI recommendation[C]// International Conference on Artificial Intelligence. 2015: 2069-2075.
[6]	BENGIO Y , COURVILLE A , VINCENT P . Representation learning:a review and new perspectives[J]. IEEE Transactions on Pattern Analysis ＆ Machine Intelligence, 2013,35(8): 1798-1828.
[7]	SAITO K , NAKANO R , KIMURA M . Prediction of information diffusion probabilities for independent cascade model[C]// International Conference on Knowledge-Based Intelligent Information and Engineering Systems. Springer-Verlag, 2008: 67-75.
[8]	SAITO K , OHARA K , YAMAGISHI Y ,et al. Learning diffusion probability based on node attributes in social networks[C]// Foundations of Intelligent Systems,International Symposium.2011. 2011: 153-162.
[9]	GOMEZ R M , LESKOVEC J , LKOPF B . Structure and dynamics of information pathways in online media[C]// ACM International Conference on Web Search and Data Mining. 2013: 23-32.
[10]	LAGNIER C , DENOYER L , GAUSSIER E ,et al. Predicting information diffusion in social networks using content and user's profiles[J]. Lecture Notes in Computer Science, 2016,7814: 74-85.
[11]	GOMEZ-RODRIGUEZ M , LESKOVEC J , KRAUSE A . Inferring networks of diffusion and Influence[C]// ACM Knowledge Discovery and Data Mining. 2011: 1019-1028.
[12]	SIMMA A , JORDAN M I . Modeling events with cascades of poisson processes[J]. Computer Science Learning, 2012: 546-555.
[13]	BOURIGAULT S , LAGNIER C , LAMPRIER S ,et al. Learning social network embeddings for predicting information diffusion[C]// ACM International Conference on Web Search and Data Mining. 2014: 393-402.
[14]	SAITO K , KIMURA M , OHARA K ,et al. Learning continuous-time information diffusion model for social behavioral data analysis[C]// Asian Conference on Machine Learning:Advances in Machine Learning.Springer-Verlag. 2009: 322-337.
[15]	RODRIGUEZ M G , BALDUZZI D , SCH?LKOPF B . Uncovering the temporal dynamics of diffusion networks[C]// International Conference on Machine Learning. 2011: 561-568.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	用户数	时间段	训练集传播片段	测试集传播片段
Digg	4 916	159	400	100
Flixster	109 816	155	800	200

数据集	模型	MSE	准确率	召回率	ROC曲线面积
	IC	0.684	0.154	0.877	0.492
Digg	Embedded IC	0.705	0.163	0.899	0.515
	NT-II	0.546	0.163	0.900	0.537
	IC	0.764	0.076	0.908	0.478
Flixster	Embedded IC	0.874	0.080	0.954	0.503
	NT- II	0.644	0.079	0.970	0.505

Social network information diffusion model based on user’s influence and interesting

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 15

Related Articles 5

Metrics

Recommended 0

[1]	Xiayu XIANG, Jiahui WANG, Zirui WANG, Shaoming DUAN, Hezhong PAN, Rongfei ZHUANG, Peiyi HAN, Chuanyi LIU. Generate medical synthetic data based on generative adversarial network [J]. Journal on Communications, 2022, 43(3): 211-224.
[2]	Zhuo CHEN, Miao ZHU, Junwei DU. Multi-view graph neural network for fraud detection algorithm [J]. Journal on Communications, 2022, 43(11): 225-232.
[3]	Chen CHEN, Yafeng RONG, Chaoqun JI, Deyun CHEN, Yongjun HE. Speaker verification method based on deep information divergence maximization [J]. Journal on Communications, 2021, 42(7): 231-237.
[4]	Ao LI, Zhuo WANG, Xiaoyang YU, Deyun CHEN, Yingtao ZHANG, Guanglu SUN. Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning [J]. Journal on Communications, 2021, 42(11): 193-204.
[5]	Bin ZHANG, Renjie LIAO. Malicious domain name detection method based on associated information extraction [J]. Journal on Communications, 2021, 42(10): 162-172.