Abstract
Given a graph over which defects, viruses, or contagions spread, leveraging a set of highly correlated subgraphs is an appealing research area with many applications. However, the challenges abound. Firstly, an initial defect in one node can cause different defects in other nodes. Second, while the time is the most significant medium to understand diffusion processes, it is not clear when the members of a subgraph may change. Third, given a pair of nodes, a contagion can spread in both directions. Previous works only consider the sequential time-window and suppose that the contagion may spread from one node to the other during a predefined time span. But the propagation can differ in various temporal dimensions (e.g. hours and days). Therefore, we propose a framework that takes both sequential and multi-aspect attributes of the time into consideration. Moreover, we devise an empirical model to estimate how frequently the subgraphs may reshape. Experiment show that our framework can effectively leverage the reshaping subgraphs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 57–66. ACM (2001)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Medlock, J., Galvani, A.P.: Optimizing influenza vaccine distribution. Science 325(5948), 1705–1708 (2009)
Valente, T.W., Pitts, S.R.: An appraisal of social network theory and analysis as applied to public health: challenges and opportunities. Annu. Rev. Pub. Health 38, 103–118 (2017)
Babishin, V., Taghipour, S.: Optimal maintenance policy for multicomponent systems with periodic and opportunistic inspections and preventive replacements. Appl. Math. Model. 40(23), 10480–10505 (2016)
Cauchi, N., Macek, K., Abate, A.: Model-based predictive maintenance in building automation systems with user discomfort. Energy 138, 306–315 (2017)
Hua, W., Wang, Z., Wang, H., Zheng, K., Zhou, X.: Short text understanding through lexical-semantic analysis. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 495–506. IEEE (2015)
Hosseini, S., Unankard, S., Zhou, X., Sadiq, S.: Location oriented phrase detection in microblogs. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 495–509. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05810-8_33
Peng, S., Wang, G., Zhou, Y., Wan, C., Wang, C., Yu, S.: An immunization framework for social networks through big data based influence modeling. IEEE Trans. Dependable Secure Comput (2017)
Gomez Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1019–1028. ACM (2010)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)
Hosseini, S., Yin, H., Zhang, M., Zhou, X., Sadiq, S.: Jointly modeling heterogeneous temporal properties in location recommendation. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 490–506. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_31
Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 529–537. ACM (2011)
Zhang, Y., Adiga, A., Saha, S., Vullikanti, A., Prakash, B.A.: Near-optimal algorithms for controlling propagation at group scale on networks. IEEE Trans. Knowl. Data Eng. 28(12), 3339–3352 (2016)
Prakash, B.A., Chakrabarti, D., Valler, N.C., Faloutsos, M., Faloutsos, C.: Threshold conditions for arbitrary cascade models on arbitrary networks. Knowl. Inf. Syst. 33(3), 549–575 (2012)
Ghasemiesfeh, G., Ebrahimi, R., Gao, J.: Complex contagion and the weakness of long ties in social networks: revisited. In: Proceedings of the fourteenth ACM conference on Electronic Commerce, pp. 507–524. ACM (2013)
Khalil, E.B., Dilkina, B., Song, L.: Scalable diffusion-aware optimization of network topology. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1226–1235. ACM (2014)
Prakash, B.A., Beutel, A., Rosenfeld, R., Faloutsos, C.: Winner takes all: competing viruses or ideas on fair-play networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1037–1046. ACM (2012)
Ganesh, A., Massouli, L., Towsley, D.: The effect of network topology on the spread of epidemics. In: Proceedings of the IEEE INFOCOM 2005, 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 1455–1466. IEEE (2005)
Prakash, B.A., Adamic, L., Iwashyna, T., Tong, H., Faloutsos, C.: Fractional immunization in networks. In: Proceedings of the 2013 SIAM International Conference on Data Mining, SIAM 2013, pp. 659–667 (2013)
Roth, D.Z., Henry, B.: Social distancing as a pandemic influenza prevention measure: evidence review. National Collaborating Centre for Infectious Diseases (2011)
Shim, E.: Optimal strategies of social distancing and vaccination against seasonal influenza. Math. Biosci. Eng. 10, 1615–1634 (2013)
Cohen, R., Havlin, S., Ben-Avraham, D.: Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91(24), 247901 (2003)
Saha, S., Adiga, A., Prakash, B.A., Vullikanti, A.K.S.: Approximation algorithms for reducing the spectral radius to control epidemic spread. In: Proceedings of the 2015 SIAM International Conference on Data Mining, SIAM 2015, pp. 568–576 (2015)
Zhang, Y., Prakash, B.A.: Dava: distributing vaccines over networks under prior information. In: Proceedings of the 2014 SIAM International Conference on Data Mining, SIAM 2014, pp. 46–54 (2014)
Aspnes, J., Chang, K., Yampolskiy, A.: Inoculation strategies for victims of viruses and the sum-of-squares partition problem. In: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp. 43–52 (2005)
Chen, C., Tong, H., Prakash, B.A., Tsourakakis, C.E., Eliassi-Rad, T., Faloutsos, C., Chau, D.H.: Node immunization on large graphs: theory and algorithms. IEEE Trans. Knowl. Data Eng. 28(1), 113–126 (2016)
Acknowledgment
This work was supported by both ST Electronics and the National Research Foundation (NRF), Prime Minister’s Office, Singapore under Corporate Laboratory @ University Scheme (Programme Title: STEE Infosec - SUTD Corporate Laboratory).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hosseini, S., Yin, H., Cheung, NM., Leng, K.P., Elovici, Y., Zhou, X. (2018). Exploiting Reshaping Subgraphs from Bilateral Propagation Graphs. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)