Abstract
In this paper, we formulate a new problem of extracting motif-based node roles from a graph with edge uncertainty, where we first count roles for each node in each sampled graph, second calculate similarities between nodes in terms of role frequency and divide all nodes into clusters according to the similarity of roles. To achieve good accuracy of role extraction for uncertain graphs, a huge amount of graph sampling, role counting, similarity calculation, or clustering is needed. For efficiently extracting node-roles from a large-scale uncertain graph, we propose four ensemble methods and compare the similarity of results and efficiency. From the experiments using four different types of real networks with probabilities assigned to their edges, we confirmed that the graph-ensemble method is much more efficient in terms of execution time compared to other ensemble methods equipped with the state-of-the-art motif counting algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
More detail algorithm will be presented in the forthcoming papers.
- 2.
For a small-size graph, Celegans, we set to \(K=5\).
References
Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N.: Efficient graphlet counting for large networks. In: 2015 IEEE International Conference on Data Mining, pp. 1–10 (2015)
Grochow, J.A., Kellis, M.: Network motif discovery using subgraph enumeration and symmetry-breaking. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS, vol. 4453, pp. 92–106. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71681-5_7
Itzhack, R., Mogilevski, Y., Louzoun, Y.: An optimal algorithm for counting network motifs. Physica A: Stat. Mech. Appl. 381, 482–490 (2007). https://www.sciencedirect.com/science/article/pii/S0378437107002257
Kvålseth, T.O.: On normalized mutual information: measure derivations and properties. Entropy 19(11) (2017). https://www.mdpi.com/1099-4300/19/11/631
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Ma, C., Cheng, R., Lakshmanan, L.V.S., Grubenmann, T., Fang, Y., Li, X.: LINC: a motif counting algorithm for uncertain graphs. Proc. VLDB Endow. 13(2), 155–168 (2019). https://doi.org/10.14778/3364324.3364330
McDonnell, M.D., Yaveroglu, O.N., Schmerl, B.A., Iannella, N., Ward, L.M.: Motif-role-fingerprints: the building-blocks of motifs, clustering-coefficients and transitivities in directed networks. Plos One 9(12), 1–25 (2014). https://doi.org/10.1371/journal.pone.0114503
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science (New York, N.Y.) 298(5594), 824–827 (2002)
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions. Math. Program. 14, 265–294 (1978)
Ohnishi, T., Takayasu, H., Takayasu, M.: Network motifs in an inter-firm network. J. Econ. Interact. Coord. 5(2), 171–180 (2010)
Pinar, A., Seshadhri, C., Vishal, V.: Escape: efficiently counting all 5-vertex subgraphs. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Republic and Canton of Geneva, CHE, pp. 1431–1440 (2017). https://doi.org/10.1145/3038912.3052597
Tran, N., Choi, K.P., Zhang, L.: Counting motifs in the human interactome. Nature Commun. 4, 2241 (2013)
Wernicke, S.: A faster algorithm for detecting network motifs. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS, vol. 3692, pp. 165–177. Springer, Heidelberg (2005). https://doi.org/10.1007/11557067_14
Acknowledgments
This material is based upon work supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 20K11940) and Early-Career Scientists (No. 19K20417).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Naito, S., Fushimi, T. (2022). Motif-Role Extraction in Uncertain Graph Based on Efficient Ensembles. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-93409-5_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93408-8
Online ISBN: 978-3-030-93409-5
eBook Packages: EngineeringEngineering (R0)