Abstract
Paths in online social networks play different roles in information dissemination. Ranking edges and identifying the influential edges in OSNs are imperative for intervening in or guiding information diffusion. The existing studies are limited to measuring the weight of paths in networks or the impact of paths on network connectivity while ignoring measuring the role of paths in information diffusion. In this study, we solve the problem by constructing an interactive network and calculating the information diffusion capacity (IDC) of paths. First, a multi-topic relationship strength network is constructed to describe users’ interactive preferences. Second, we propose the IDC method to measure the IDC of paths considering users’ interactive activity, relationship strength, users’ influence and users’ topic preferences. Users’ multi-topic relationship strength makes it possible to distinguish the importance of edges under different topics. Finally, we compare the IDC method with the baseline methods including the connectivity-based method and the method of edge betweenness centrality from the perspectives of ranking granularity and spreading ability in two subnets from Sina Weibo and Twitter. The results show that the IDC method ranks edges in finer grain than the connectivity-based method. Based on the SIR model, we compare the spreading ability of the three methods and find that the important edges evaluated by the IDC method behave better in topic spreading. Application exploration shows that the backbone network and the weak ties in information diffusion can be discovered through evaluating the paths in the relationship strength network.












Similar content being viewed by others
References
Curiskis SA, Drake B, Osborn TR, Kennedy PJ (2020) An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit. Inf Process Manag 57(2):102034. https://doi.org/10.1016/j.ipm.2019.04.002
Osho A, Goodman C, Amariucai G (2020) MIDMod-OSN: a microscopic-level information diffusion model for online social networks. In: Chellappan S, Choo K-KR, Phan N (eds) Computational data and social networks. Springer, Berlin, pp 437–450. https://doi.org/10.1007/978-3-030-66046-8_36
Molaei S, Zare H, Veisi H (2020) Deep learning approach on information diffusion in heterogeneous networks. Knowl-Based Syst 189:105153. https://doi.org/10.1016/j.knosys.2019.105153
Molaei S, Babaei S, Salehi M, Jalili M (2018) Information spread and topic diffusion in heterogeneous information networks. Sci Rep 8(1):9549. https://doi.org/10.1038/s41598-018-27385-2
Gui H, Sun Y, Han J, Brova G (2014) Modeling topic diffusion in multi-relational bibliographic information networks. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pp 649–658. https://doi.org/10.1145/2661829.2662000
Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90. https://doi.org/10.1126/science.1116869
Manuel G, Jure L, David B, Bernhard S (2014) Uncovering the structure and temporal dynamics of information propagation. Netw Sci 2(01):26–65. https://doi.org/10.3929/ethz-b-000094313
Peixoto T (2019) Network reconstruction and community detection from dynamics. Phys Rev Lett. https://doi.org/10.1103/PhysRevLett.123.128301
Zigron S, Bronstein J (2019) “Help is where you find it”: the role of weak ties networks as sources of information and support in virtual health communities. JASIST 7(2):130–139. https://doi.org/10.1002/asi.24106
Lyu D, Yuan Y, Wang L, Wang X, Pentland A (2022) Investigating and modeling the dynamics of long ties. Commun Phys 5(1):87. https://doi.org/10.1038/s42005-022-00863-w
Zheng J, Li Q, Liao J, Wang S (2021) Explainable link prediction based on multi-granularity relation-embedded representation. Knowl-Based Syst 230:107402. https://doi.org/10.1016/j.knosys.2021.107402
Dhelim S, Aung N, Ning H (2020) Mining user interest based on personality-aware hybrid filtering in social networks. Knowl-Based Syst 206:106227. https://doi.org/10.1016/j.knosys.2020.106227
Fu X, Zhang J, Meng Z, King I (2020) MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference vo. 2020, pp 2331–2341. https://doi.org/10.1145/3366423.3380297
Manuel G, Jure L, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data 5(4):1–37. https://doi.org/10.1145/2086737.2086741
Manuel G, Jure L, Bernhard S (2013) Structure and dynamics of information pathways in online media. In: Proceedings of the sixth ACM international conference on web search and data mining, pp 23–32
Li C, Lin Y, Yeh M (2018) Forecasting participants of information diffusion on social networks with its applications. Inf Sci 422:432–446. https://doi.org/10.1016/j.ins.2017.09.034
Liu W, Deng Z, Cao L, Xu X, Liu H, Gong X (2017) Mining top k spread sources for a specific topic and a given node. IEEE Trans Cybern 45(11):2472–2483. https://doi.org/10.1109/TCYB.2014.2375185
Zhu H, Yin X, Ma J, Hu W (2016) Identifying the main paths of information diffusion in online social networks. Physica A 452:320–328. https://doi.org/10.1016/j.physa.2016.01.048
Gao C, Wei D, Hu Y, Sankaran M, Yong D (2014) A modified evidential methodology of identifying influential nodes in weighted networks. Physica A 392(21):5490–5500. https://doi.org/10.1016/j.physa.2013.06.059
Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41. https://doi.org/10.2307/3033543
Freeman L (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239
Li C, Wang L, Sun S, Xia C (2018) Identification of influential spreaders based on classified neighbors in real-world complex networks. Appl Math Comput 320:512–523. https://doi.org/10.1016/j.amc.2017.10.001
Zareie A, Sheikhahmadi A, Jalili M, Fasaei MSK (2020) Finding influential nodes in social networks based on neighborhood correlation coefficient. Knowl-Based Syst 194:105580. https://doi.org/10.1016/j.knosys.2020.105580
Samanta S, Dubey VK, Sarkar B (2021) Measure of influences in social networks. Appl Soft Comput 99:106858. https://doi.org/10.1016/j.asoc.2020.106858
Weng J, Lim E, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proc. of the 3rd ACM international conf. on web search and data mining, pp 261–270. https://doi.org/10.1145/1718487.1718520
An L, Zhou W, Ou M, Li G, Yu C, Wang X (2021) Measuring and profiling the topical influence and sentiment contagion of public event stakeholders. Int J Inf Manag 58:102327. https://doi.org/10.1016/j.ijinfomgt.2021.102327
Bahutair M, Al Aghbari Z, Kamel I (2022) NodeRank: finding influential nodes in social networks based on interests. J Supercomput 78(2):2098–2124. https://doi.org/10.1007/s11227-021-03947-6
Albert R, Barabási A (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97. https://doi.org/10.1103/RevModPhys.74.47
Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177. https://doi.org/10.1080/0022250X.2001.9990249
Sosnowska J, Skibski O (2018) Path evaluation and centralities in weighted graphs—an axiomatic approach. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence main track, vol 536, pp 3856–3862. https://doi.org/10.24963/ijcai.2018/536
Wu R, Zhou Y, Chen Z (2019) Identifying urban traffic bottlenecks with percolation theory. Urban Transp China 17(01):96–101. https://doi.org/10.13813/j.cn11-5141/u.2019.0002
Wang S, Li C, Wang Z, Chen H, Zheng K (2020) BPF++: a unified factorization model for predicting retweet behaviors. Inf Sci. https://doi.org/10.1016/j.ins.2019.12.017
Dai T, Xiao Y, Liang X, Li Q, Li T (2022) ICS-SVM: a user retweet prediction method for hot topics based on improved SVM. Digital Commun Netw 8(2):186–193. https://doi.org/10.1016/j.dcan.2021.07.003
Yin X, Wang H, Yin P, Zhu H (2019) Agent-based opinion formation modeling in social network: a perspective of social psychology. Physica A 532:121786. https://doi.org/10.1016/j.physa.2019.121786
Tang J, Sun J, Wang C, Yang Z (2009) Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 807–816. https://doi.org/10.1145/1557019.1557108
Ju C, Tao W (2017) A novel relationship strength model for online social networks. Multimed Tools Appl 76(16):17577–17594. https://doi.org/10.1007/s11042-017-4408-4
Jin Z, Liu R, Li Q, Zeng D, Zhan Y, Wang L (2016) Predicting user's multi-interests with network embedding in health-related topics. In: 2016 International joint conference on neural networks (IJCNN), pp 2568–2575. https://doi.org/10.1109/IJCNN.2016.7727520
Blei D, Ng A, Jordan M (2003) Latent Dirichlet Allocation. J Mach Learn Res 3:993–1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993
Lü L, Chen D, Zhou T (2011) The small world yields the most effective information spreading. New J Phys 13(12):123005. https://doi.org/10.1088/1367-2630/13/12/123005
Li Q, Zhou T, Lü L, Chen D (2014) Identifying influential spreaders by weighted LeaderRank. Physica A. https://doi.org/10.1016/j.physa.2014.02.041
Nicosia V, Tang J, Musolesi M, Russo G, Mascolo C, Latora V (2012) Components in time-varying graphs. Chaos Interdiscip J Nonlinear Sci 22(2):023101. https://doi.org/10.1063/1.3697996
Han W, Zhu X, Zhu Z, Chen W, Zheng W, Lu J (2016) A comparative analysis on Weibo and Twitter. Tsinghua Sci Technol 21(1):1–16. https://doi.org/10.1109/TST.2016.7399279
Fatemi B, Molaei S, Pan S, Rahimi SA (2022) GCNFusion: An efficient graph convolutional network based model for information diffusion. Expert Syst Appl 202:117053. https://doi.org/10.1016/j.eswa.2022.117053
Filiposka S, Gajduk A, Dimitrova T, Kocarev L (2017) Bridging online and offline social networks: multiplex analysis. Physica A 471:825–836. https://doi.org/10.1016/j.physa.2016.12.050
He W, Ai D, Wu C (2021) A recommender model based on strong and weak social ties: a long-tail distribution perspective. Expert Syst Appl 184:115483. https://doi.org/10.1016/j.eswa.2021.115483
Jia J, Chen Y, Li Y, Li T, Chen N (2021) Effect of weak ties on degree and H-index in link prediction of complex network. Mod Phys Lett B 35(18):2150301. https://doi.org/10.1142/S0217984921503012
Acknowledgements
This work is supported by the National Natural Science Foundation of China [Grant Numbers 71874088, 71704085]; the Cultivation Base of Excellent Innovation Team in Philosophy & Social Sciences in Jiangsu Universities [Grant Number 2017ZSTD022].
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, H., Yang, X., Wei, J. et al. Evaluation of information diffusion path based on a multi-topic relationship strength network. Knowl Inf Syst 65, 1199–1220 (2023). https://doi.org/10.1007/s10115-022-01794-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-022-01794-2