Abstract
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors, incomplete management information and dependency relationship between the entities among the emerging complex networks. Due to the complexity of emerging networks, it is not always possible to build precise models in modeling and optimization (local and global) for networks. This paper presents a survey on probabilistic modeling for evolving networks and identifies the new challenges which emerge on the probabilistic models and optimization strategies in the potential application areas of network performance, network management and network security for evolving networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1) (January 2002)
Aiello, W., Chung, F., Lu, L.: A random graph model for power law graphs. Exp. Math. 10, 53–66 (2001)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Berzuini, C.: Representing Time in Causal Probabilistic Networks. In: Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence, vol. 10, pp. 15–28 (1989)
Bilgin, C.C.: Dynamic network evolution: models, clustering, anomaly detection. Technical Report, Rensselaer University, NY (2008)
Björkman, K., Karanta, I.: A Dynamic Flowgraph Methodology Approach Based on Binary Decision Diagrams. In: Proceedings of International Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, pp. 267–278 (2011)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5, 157–166 (1994)
Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. CoRR, abs/1012.0009 (2010)
Ding, J.: Probabilistic Fault Management in Distributed Systems. VDI Verlag, Germany (2008) ISSN: 0178-9627, ISBN: 978-3-18-379110-1
Ding, J.: Advances in Network Management. CRC Press, Taylor & Francis Group (2010) ISBN-13: 978-1420064520
Ding, J., Bouvry, P., Balasingham, I.: Management Challenges for Emerging Wireless Networks. In: Makaya, C., Pierre, S. (eds.) Emerging Wireless Networks: Concepts, Techniques and Applications, pp. 3–34. CRC Press (2012) ISBN: 9781439821350
Erdős, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960)
Gong, S., Xiang, T.: Recognition of group activities using dynamic probabilistic networks. In: ICCV, pp. 742–749 (2003)
Harary, F., Gupta, G.: Dynamic graph models. Mathl. Comput. Modelling, 79–87 (1997)
Flesch, I., Lucas, P.: Independence decomposition in dynamic Bayesian networks. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 560–571. Springer, Heidelberg (2007)
Lua, E.K., Crowcroft, J., Pias, M., Sharma, R., Lim, S.: A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys & Tutorials 7(2), 72–93 (2005)
Lin, C.-Y.: Information flow prediction by modeling dynamic probabilistic social network. In: Proc. Int. Conf. Netw. Sci. (2007)
Lekuona, A., Lacruz, B., Lasala, P., Fisher, D., Lenz, H.J.: Modeling and monitoring dynamic systems by chain graphs. In: Learning from Data: Artificial Intelligence and Statistics V. Lecture Notes in Statistics, vol. 112, pp. 69–77. Springer-Verlag (1996)
Mirowski, P., LeCun, Y.: Dynamic Factor Graphs for Time Series Modeling. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 128–143. Springer, Heidelberg (2009)
Mihajlovic, V., Petkovic, M.: Dynamic Bayesian Networks: A State of the Art. Technical Report TR-CTIT-01-34, Centre for Telematics and Information Technology University of Twente, Enschede (2001) ISSN 1381-3625
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California (2002)
Newman, M.E.J.: The Structure and Function of Complex Networks. SIAM Rev. 45, 167–256 (2003)
Peköz, E., Röllin, A., Ross, N.: Total variation error bounds for geometric approximation. Bernoulli 19(2), 610–632 (2013)
Strogatz, S.: Exploring complex networks. Nature 410, 268–276 (2001)
Tucker, A., Liu, X.: Learning dynamic bayesian networks from multivariate time series with changing dependencies. In: Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 100–110. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ding, J., Bouvry, P. (2014). Challenges on Probabilistic Modeling for Evolving Networks. In: Schuetze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol 500. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01460-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-01460-9_4
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01459-3
Online ISBN: 978-3-319-01460-9
eBook Packages: EngineeringEngineering (R0)