Journal Home > Volume 2 , Issue 2
Journal of Social Computing 2021, 2(2): 166-182 https://doi.org/10.23919/JSC.2021.0007
Open Access | Issue | Published: 23 August 2021

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

Show Author's Information Chenting Jiang1,  Anthony D’Arienzo2,  Weihua Li3( ),  Shiqing Wu1,  Quan 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
Keywords:
influence maximization, influence diffusion, complex social networks, operator-based model, heat diffusion-based influence modeling
Cite this article:
Jiang C, D’Arienzo A, Li W, et al. An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks. Journal of Social Computing, 2021, 2(2): 166-182. https://doi.org/10.23919/JSC.2021.0007
Download citation
EndNote(RIS)
BibTeX

913

Views

57

Downloads

Citations

8

Crossref

N/A

WoS

8

Scopus

N/A

CSCD

Abstract Full text About this article

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.


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Chenting Jiang1, Anthony D’Arienzo2, Weihua Li3( ), Shiqing Wu1, Quan 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 maximization, influence diffusion, complex social networks, operator-based model, heat diffusion-based influence modeling

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.
DOI Google Scholar
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.
DOI Google Scholar
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 Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
35

M. Granovetter, Threshold models of collective behavior, American Journal of Sociology, vol. 83, no. 6, pp. 1420–1443, 1978.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
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.
DOI Google Scholar
Publication history
Rights and permissions

Publication history

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

Rights and permissions

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/).

Return