research-article

NodeRank: Finding influential nodes in social networks based on interests

Authors:

Mohammed Bahutair,

Zaher Al Aghbari,

Ibrahim KamelAuthors Info & Claims

The Journal of Supercomputing, Volume 78, Issue 2

Pages 2098 - 2124

https://doi.org/10.1007/s11227-021-03947-6

Published: 01 February 2022 Publication History

Abstract

Finding influential members in social networks received a lot of interest in recent literature. Several algorithms have been proposed that provide techniques for extracting a set of the most influential people in a certain social network. However, most of these algorithms find influential nodes based solely on the topological structure of the network. In this paper, a new algorithm, namely NodeRank, is proposed that ranks every user in a given social network based on the topological structure as well as the interests of the users (nodes). Higher ranks are given to people with great influence on other members of the network. Furthermore, the paper investigates a MapReduce version of the algorithm that enables the algorithm to run on multiple machines simultaneously. Experiments showed that the MapReduce model is not suitable for the NodeRank algorithm since MapReduce is only applicable for batch processes and the NodeRank is highly iterative. For that reason, a parallel version of the algorithm is proposed that utilizes Hadoop Spark, a framework for parallel processes that supports batch operations as well as iterative and recursive algorithms. Several experiments have been carried out to test the accuracy as well as the scalability of the algorithm.

References

[1]

Said A, Abbasi RA, Maqbool O, Daud A, and Aljohani NR Cc-ga: A clustering coefficient based genetic algorithm for detecting communities in social networks Appl Soft Comput 2018 63 59-70

[2]

Zhao Z, Li C, Zhang X, Chiclana F, and Viedma EH An incremental method to detect communities in dynamic evolving social networks Knowl-Based Syst 2019 163 404-415

[3]

Al-Garadi MA, Varathan KD, Ravana SD, Ahmed E, Mujtaba G, Khan MUS, and Khan SU Analysis of online social network connections for identification of influential users: Survey and open research issues ACM Comput Surv (CSUR) 2018 51 1 1-37

[4]

Khomami MMD, Rezvanian A, Meybodi MR, and Bagheri A Cfin: A community-based algorithm for finding influential nodes in complex social networks J Supercomput 2020 77 2021 2207-2236

[5]

Zaharia M, Chowdhury M, Franklin MJ, Shenker S, and Stoica I Spark: Cluster computing with working sets HotCloud 2010 10 10 95

[6]

Aldrich DP The importance of social capital in building community resilience 2017 Cham Springer 357-364

[7]

Al Aghbari Z, Bahutair M, Kamel I (2019) “Geosimmr: A mapreduce algorithm for detecting communities based on distance and interest in social networks,” Data Science Journal, 18(1),

[8]

Huang LV and Liu PL Ties that work: Investigating the relationships among coworker connections, work-related facebook utility, online social capital, and employee outcomes Comput Hum Behav 2017 72 512-524

[9]

Richardson M, Domingos P (2002) “Mining knowledge-sharing sites for viral marketing,” in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 61–70

[10]

Kempe D, Kleinberg J, and Tardos É Maximizing the spread of influence through a social network Theory Comput 2015 11 4 105-147

[11]

Shakarian P, Bhatnagar A, Aleali A, Shaabani E, and Guo R The independent cascade and linear threshold models Diffusion in Social Networks 2015 Cham Springer 35-48

[12]

Narayanam R and Narahari Y A shapley value-based approach to discover influential nodes in social networks IEEE Trans Autom Sci Eng 2011 8 1 130-147

[13]

Papapetrou P, Gionis A, Mannila H (2011) “A shapley value approach for influence attribution,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp. 549–564

[14]

Chen W, Wang Y, Yang S (2009) “Efficient influence maximization in social networks,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 199–208

[15]

Ben-Zwi O, Hermelin D, Lokshtanov D, Newman I (2009) “An exact almost optimal algorithm for target set selection in social networks,” in Proceedings of the 10th ACM conference on Electronic commerce. ACM, pp. 355–362

[16]

Chen N On the approximability of influence in social networks SIAM J Discrete Math 2009 23 3 1400-1415

[17]

Singer Y (2012)“How to win friends and influence people, truthfully: influence maximization mechanisms for social networks,” in Proceedings of the fifth ACM international conference on Web search and data mining. ACM, pp. 733–742

[18]

Khorasgani RR, Chen J, Zaïane OR (2010) “Top leaders community detection approach in information networks,” in 4th SNA-KDD Workshop on Social Network Mining and Analysis. Citeseer, Washington DC

[19]

Hosseini R and Rezvanian A Antlp: ant-based label propagation algorithm for community detection in social networks CAAI Trans Intell Technol 2020 5 1 34-41

[20]

Li X, Cao C, Zhang T (2020) “Block diagonal dominance-based dynamic programming for detecting community,” The Journal of Supercomputing, pp. 1–14

[21]

Goyal A, Bonchi F, Lakshmanan LV (2008) “Discovering leaders from community actions,” in Proceedings of the 17th ACM conference on Information and knowledge management. ACM, pp. 499–508

[22]

Agarwal N, Liu H, Tang L, Yu PS (2008) “Identifying the influential bloggers in a community,” in Proceedings of the 2008 international conference on web search and data mining. ACM, pp. 207–218

[23]

Ilyas MU, Radha H (2011) “Identifying influential nodes in online social networks using principal component centrality,” in 2011 IEEE International Conference on Communications (ICC). IEEE, pp. 1–5

[24]

Ilyas MU, Radha H (2010) “A klt-inspired node centrality for identifying influential neighborhoods in graphs,” in Information Sciences and Systems (CISS), 2010 44th Annual Conference on. IEEE, pp. 1–7

[25]

Zareie A, Sheikhahmadi A, and Jalili M Influential node ranking in social networks based on neighborhood diversity Future Gener Comput Syst 2019 94 120-129

[26]

Cha M, Haddadi H, Benevenuto F, and Gummadi PK Measuring user influence in twitter: The million follower fallacy ICWSM 2010 10 10–17 30

[27]

Romero DM, Galuba W, Asur S, Huberman BA (2011) “Influence and passivity in social media,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp. 18–33

[28]

Aral S and Walker D Identifying influential and susceptible members of social networks Science 2012 337 6092 337-341

[29]

Fang Q, Sang J, Xu C, and Rui Y Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning IEEE Trans Multimedia 2014 16 3 796-812

[30]

Amato F, Moscato V, Picariello A, Sperlí G (2017) “Influence maximization in social media networks using hypergraphs,” in International Conference on Green, Pervasive, and Cloud Computing. Springer, pp. 207–221

[31]

Miller GA Wordnet: A lexical database for english Commun ACM 1995 38 11 39-41

[32]

Lin D (1998) “An information-theoretic definition of similarity,” in Proceedings of the Fifteenth International Conference on Machine Learning, ser. ICML ’98. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 296–304

[33]

Dean J and Ghemawat S Mapreduce: simplified data processing on large clusters Commun ACM 2008 51 1 107-113

[34]

Zheng C, Wang J, Jain A (2015) “All-pairs shortest paths in spark,”

Cited By

Akhavan-Hejazi ZEsmaeili MGhobaei-Arani MMinaei-Bidgoli B(2024)Identifying influential users using homophily-based approach in location-based social networksThe Journal of Supercomputing10.1007/s11227-024-06228-080:13(19091-19126)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06228-0
Rashid YBhat J(2024)An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic reviewMultimedia Systems10.1007/s00530-023-01258-930:1Online publication date: 3-Feb-2024
https://dl.acm.org/doi/10.1007/s00530-023-01258-9
Seyfosadat SRavanmehr R(2023)Systematic literature review on identifying influencers in social networksArtificial Intelligence Review10.1007/s10462-023-10515-256:Suppl 1(567-660)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10462-023-10515-2
Show More Cited By

Index Terms

NodeRank: Finding influential nodes in social networks based on interests
1. Information systems
  1. Information systems applications
    1. Data mining
2. Software and its engineering

Index terms have been assigned to the content through auto-classification.

Recommendations

A community-based approach to identify the most influential nodes in social networks

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information ...
Influential Nodes Identification Based on Activity Behaviors and Network Structure With Personality Analysis in Egocentric Online Social Networks

Online social network (OSN) platforms have become a place where the users share their details through various activities. Nowadays, OSNs have major impact not only on the users' lives but also on business and society. Organizations use OSN platforms to ...
Identifying communities and influential node in Facebook fan page - a case study of FJU 2013 ad camp

In this paper, we contribute to the understanding of a business-like Facebook fan page by investigating the network structure of FJU ad camp fan page created exclusively for summer camp recruitment. In our approach, we first characterise the network ...

Comments

Information & Contributors

Information

Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 78, Issue 2

Feb 2022

1527 pages

ISSN:0920-8542

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2022

Accepted: 31 May 2021

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Akhavan-Hejazi ZEsmaeili MGhobaei-Arani MMinaei-Bidgoli B(2024)Identifying influential users using homophily-based approach in location-based social networksThe Journal of Supercomputing10.1007/s11227-024-06228-080:13(19091-19126)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06228-0
Rashid YBhat J(2024)An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic reviewMultimedia Systems10.1007/s00530-023-01258-930:1Online publication date: 3-Feb-2024
https://dl.acm.org/doi/10.1007/s00530-023-01258-9
Seyfosadat SRavanmehr R(2023)Systematic literature review on identifying influencers in social networksArtificial Intelligence Review10.1007/s10462-023-10515-256:Suppl 1(567-660)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10462-023-10515-2
Zhu HYang XWei JShen C(2023)Evaluation of information diffusion path based on a multi-topic relationship strength networkKnowledge and Information Systems10.1007/s10115-022-01794-265:3(1199-1220)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10115-022-01794-2
Yu AWang N(2022)Node-importance ranking in scale-free networks: a network metric response model and its solution algorithmThe Journal of Supercomputing10.1007/s11227-022-04544-x78:15(17450-17469)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11227-022-04544-x

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents