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Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.


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An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Show Author's information Chenting Jiang1Anthony D’Arienzo2Weihua Li3( )Shiqing Wu1Quan Bai1( )
School of Information and Communication Technology, University of Tasmania, Hobart 7005, Australia
Department of Mathematics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand

Abstract

Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.

Keywords:

influence diffusion, influence maximization, complex social networks, operator-based model, heat diffusion-based influence modeling
Received: 31 May 2021 Revised: 14 June 2021 Accepted: 22 June 2021 Published: 23 August 2021 Issue date: June 2021
References(50)
1
X. Y. Kong, Z. Q. Gu, and L. H. Yin, A unified information diffusion model for social networks, presented at 2020 IEEE 5th Int. Conf. Data Science in Cyberspace (DSC), Hong Kong, China, 2020, pp. 38−44.https://doi.org/10.1109/DSC50466.2020.00014
DOI
2

S. S. Singh, A. Kumar, K. Singh, and B. Biswas, C2IM: Community based context-aware influence maximization in social networks, Physica A:Statistical Mechanics and its Applications, vol. 514, pp. 796–818, 2019.

3

X. Y. Liu, D. B. He, and C. Liu, Information diffusion nonlinear dynamics modeling and evolution analysis in online social network based on emergency events, IEEE Transactions on Computational Social Systems, vol. 6, no. 1, pp. 8–19, 2019.

4

L. F. Zhang, C. Su, Y. F. Jin, M. Goh, and Z. Y. Wu, Cross-network dissemination model of public opinion in coupled networks, Information Sciences, vols. 451&452, pp. 240–252, 2018.

DOI
5

L. J. Zhang, T. Wang, Z. L. Jin, N. Su, C. H. Zhao, and Y. J. He, The research on social networks public opinion propagation influence models and its controllability, China Communications, vol. 15, no. 7, pp. 98–110, 2018.

6

W. J. Wang and W. N. Street, Modeling and maximizing influence diffusion in social networks for viral marketing, Applied Network Science, vol. 3, no. 1, p. 6, 2018.

7
D. Kempe, J. Kleinberg, and E. Tardos, Maximizing the spread of influence through a social network, in Proc. 9th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Washington, DC, USA, 2003, pp. 137−146.https://doi.org/10.1145/956750.956769
DOI
8
S. Bhagat, A. Goyal, and L. V. S. Lakshmanan, Maximizing product adoption in social networks, in Proc. 5th ACM Int. Conf. Web Search and Data Mining, Washington, DC, USA, 2012, pp. 603−612.https://doi.org/10.1145/2124295.2124368
DOI
9

L. Zhu and Y. G. Wang, Rumor diffusion model with spatio-temporal diffusion and uncertainty of behavior decision in complex social networks, Physica A:Statistical Mechanics and its Applications, vol. 502, pp. 29–39, 2018.

10

S. Shelke and V. Attar, Source detection of rumor in social network-A review, Online Social Networks and Media, vol. 9, pp. 30–42, 2019.

11

Y. P. Xiao, Q. F. Yang, C. Y. Sang, and Y. B. Liu, Rumor diffusion model based on representation learning and anti-rumor, IEEE Transactions on Network and Service Management, vol. 17, no. 3, pp. 1910–1923, 2020.

12
W. H. Li, Comprehensive modelling of influence diffusion in complex social networks, an agent-based perspective, PhD dissertation, Auckland University of Technology, Auckland, New Zealand, 2018.
13

Z. K. Zhang, C. Liu, X. X. Zhan, X. Lu, C. X. Zhang, and Y. C. Zhang, Dynamics of information diffusion and its applications on complex networks, Physics Reports, vol. 651, pp. 1–34, 2016.

14

A. Guille, H. Hacid, C. Favre, and D. A. Zighed, Information diffusion in online social networks: A survey, ACM SIGMOD Record, vol. 42, no. 2, pp. 17–28, 2013.

15

M. Li, X. Wang, K. Gao, and S. S. Zhang, A survey on information diffusion in online social networks: Models and methods, Information, vol. 8, no. 4, p. 118, 2017.

16
K. Saito, K. Ohara, Y. Yamagishi, M. Kimura, and H. Motoda, Learning diffusion probability based on node attributes in social networks, presented at Int. Symp. Methodologies for Intelligent Systems, Warsaw, Poland, 2011, pp. 153−162.https://doi.org/10.1007/978-3-642-21916-0_18
DOI
17
A. Guille and H. Hacid, A predictive model for the temporal dynamics of information diffusion in online social networks, in Proc. 21st Int. Conf. World Wide Web, Lyon, France, 2012, pp. 1145−1152.https://doi.org/10.1145/2187980.2188254
DOI
18
C. Lagnier, L. Denoyer, E. Gaussier, and P. Gallinari, Predicting information diffusion in social networks using content and user’s profiles, presented at European Conf. Information Retrieval, Moscow, Russia, 2013, pp. 74−85.https://doi.org/10.1007/978-3-642-36973-5_7
DOI
19

N. Barbieri, F. Bonchi, and G. Manco, Topic-aware social influence propagation models, Knowledge and Information Systems, vol. 37, no. 3, pp. 555–584, 2013.

20
N. Barbieri, F. Bonchi, and G. Manco, Topic-aware social influence propagation models, presented at 2012 IEEE 12th Int. Conf. Data Mining, Brussels, Belgium, 2012, pp. 81−90.https://doi.org/10.1109/ICDM.2012.122
DOI
21
G. Golnari, A. Asiaee, A. Banerjee, and Z. L. Zhang, Revisiting non-progressive influence models: Scalable influence maximization, arXiv preprint arXiv: 1412.5718, 2018.
22

S. C. Peng, Y. M. Zhou, L. H. Cao, S. Yu, J. W. Niu, and W. J. Jia, Influence analysis in social networks: A survey, Journal of Network and Computer Applications, vol. 106, pp. 17–32, 2018.

23

M. Doo and L. Liu, Probabilistic diffusion of social influence with incentives, IEEE Transactions on Services Computing, vol. 7, no. 3, pp. 387–400, 2014.

24
H. Xu, S. Gao, Y. Zhao, J. C. Li, H. C. Pang, and J. Guo, Predicting information diffusion via matrix factorization based model, presented at 2014 4th IEEE Int. Conf. Network Infrastructure and Digital Content, Beijing, China, 2014, pp. 257−261.
25

M. Timilsina, M. Tandan, M. d’Aquin, and H. X. Yang, Discovering links between side effects and drugs using a diffusion based method, Scientific Reports, vol. 9, no. 1, p. 10436, 2019.

26
P. P. Van Maanen and B. Van der Vecht, An agent-based approach to modeling online social influence, presented at 2013 IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining (ASONAM 2013), Niagara Falls, Canada, 2013, pp. 600−607.https://doi.org/10.1145/2492517.2492564
DOI
27

W. Li, Q. Bai, and M. Zhang, A multi-agent system for modelling preference-based complex influence diffusion in social networks, The Computer Journal, vol. 62, no. 3, pp. 430–447, 2019.

28
H. R. Nasrinpour, M. R. Friesen, and R. D. McLeod, An agent-based model of message propagation in the Facebook electronic social network, arXiv preprint arXiv: 1611.07454, 2016.
29

E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. Suppl. 3, pp. 7280–7287, 2002.

30

M. Cinelli, G. De Francisci Morales, A. Galeazzi, W. Quattrociocchi, and M. Starnini, The echo chamber effect on social media, Proceedings of the National Academy of Sciences of the United States of America, vol. 118, no. 9, p. e2023301118, 2021.

31
H. Ma, H. Yang, M. R. Lyu, and I. King, Mining social networks using heat diffusion processes for marketing candidates selection, in Proc. 17th ACM Conf. Information and Knowledge Management, Napa Valley, CA, USA, 2008, pp. 233−242.https://doi.org/10.1145/1458082.1458115
DOI
32
M. Jaouadi and L. B. Romdhane, Influence maximization problem in social networks: An overview, presented at 2019 IEEE/ACS 16th Int. Conf. Computer Systems and Applications (AICCSA), Abu Dhabi, United Arab Emirates, 2019, pp. 1−8.https://doi.org/10.1109/AICCSA47632.2019.9035366
DOI
33

S. Chen, J. Fan, G. L. Li, J. H. Feng, K. L. Tan, and J. H. Tang, Online topic-aware influence maximization, Proceedings of the VLDB Endowment, vol. 8, no. 6, pp. 666–677, 2015.

34

J. Goldenberg, B. Libai, and E. Muller, Talk of the network: A complex systems look at the underlying process of word-of-mouth, Marketing Letters, vol. 12, no. 3, pp. 211–223, 2001.

35

M. Granovetter, Threshold models of collective behavior, American Journal of Sociology, vol. 83, no. 6, pp. 1420–1443, 1978.

36

M. Timilsina, M. d’Aquin, and H. X. Yang, Heat diffusion approach for scientific impact analysis in social media, Social Network Analysis and Mining, vol. 9, no. 1, p. 16, 2019.

37

C. K. Chou and M. S. Chen, Learning multiple factors-aware diffusion models in social networks, IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 7, pp. 1268–1281, 2018.

38
H. P. Xu, J. M. Wei, Z. L. Yang, J. H. Ruan, and J. Wang, Probabilistic topic and role model for information diffusion in social network, presented at Pacific-Asia Conf. Knowledge Discovery and Data Mining, Melbourne, Australia, 2018, pp. 3−15.https://doi.org/10.1007/978-3-319-93037-4_1
DOI
39

Y. C. Li, J. Fan, Y. H. Wang, and K. L. Tan, Influence maximization on social graphs: A survey, IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 10, pp. 1852–1872, 2018.

40

G. J. Song, Y. H. Li, X. D. Chen, X. R. He, and J. Tang, Influential node tracking on dynamic social network: An interchange greedy approach, IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 2, pp. 359–372, 2017.

41

M. Heidari, M. Asadpour, and H. Faili, SMG: Fast scalable greedy algorithm for influence maximization in social networks, Physica A:Statistical Mechanics and its Applications, vol. 420, pp. 124–133, 2015.

42
X. Y. Cai, T. Y. Xu, J. F. Yi, J. Z. Huang, and S. Rajasekaran, DTWNet: A dynamic time warping network, in Proc. 33rd Annu. Conf. neural Information Processing Systems, Vancouver, Canada, 2019, pp. 11636−11646.
43

R. S. De Sousa, A. Boukerche, and A. A. F. Loureiro, Vehicle trajectory similarity: Models, methods, and applications, ACM Computing Surveys, vol. 53, no. 5, p. 94, 2020.

44
J. Leskovec and A. Krevl, SNAP datasets: Stanford large network dataset collection, http://snap.stanford.edu/data, 2014.
45

M. De Domenico, A. Lima, P. Mougel, and M. Musolesi, The anatomy of a scientific rumor, Scientific Reports, vol. 3, p. 2980, 2013.

46
A. Jonathan, 11, 000+ expanded & coded links from 36.5k IRA Troll tweets, https://data.world/d1gi/11000-expanded-labeled-links-from-365k-troll-tweets, 2018.
47
A. Hatua, T. T. Nguyen, and A. H. Sung, Information diffusion on twitter: Pattern recognition and prediction of volume, sentiment, and influence, in Proc. 4th IEEE/ACM Int. Conf. Big Data Computing, Applications and Technologies, Austin, TX, USA, 2017, pp. 157−167.https://doi.org/10.1145/3148055.3148078
DOI
48
A. Sebernegg, P. Kán, and H. Kaufmann, Motion similarity modeling-a state of the art report, arXiv preprint arXiv: 2008.05872, 2020.
49

W. Choi, J. Cho, S. Lee, and Y. Jung, Fast constrained dynamic time warping for similarity measure of time series data, IEEE Access, vol. 8, pp. 222841–222858, 2020.

50

A. S. Haq, M. Nasrun, C. Setianingsih, and M. A. Murti, Speech recognition implementation using MFCC and DTW algorithm for home automation, Proceeding of the Electrical Engineering Computer Science and Informatics, vol. 7, no. 2, pp. 78–85, 2020.

Publication history
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Publication history

Received: 31 May 2021
Revised: 14 June 2021
Accepted: 22 June 2021
Published: 23 August 2021
Issue date: June 2021

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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