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Lyapunov–based Anomaly Detection in Preferential Attachment Networks Cover

Lyapunov–based Anomaly Detection in Preferential Attachment Networks

By: Diego Ruiz and  Jorge Finke  
Open Access
|Jul 2019

References

  1. Barabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks, Science286(5439): 509–512.10.1126/science.286.5439.50910521342
  2. Barabási, A.-L. and Pósfai, M. (2016). Network Science, Cambridge University Press, Cambridge.
  3. Bianconi, G. and Barabási, A. L. (2001). Competition and Multiscaling in evolving networks, Europhysics Letters54(4): 436–442.10.1209/epl/i2001-00260-6
  4. Burgess, K. and Passino, K. (1995). Stability analysis of load balancing systems, International Journal of Control61(2): 357–393.10.1080/00207179508921907
  5. Caldarelli, G., Capocci, A., De Los Rios, P. and Muñoz, M.A. (2002). Scale-free networks from varying vertex intrinsic fitness, Physical Review Letters89(25): 258702.10.1103/PhysRevLett.89.25870212484927
  6. Chandola, V., Banerjee, A. and Kumar, V. (2009). Anomaly detection: A survey, ACM Computing Surveys41(3): 15:1–15:58.10.1145/1541880.1541882
  7. Chen, Q. and Shi, D. (2004). The modeling of scale-free networks, Physica A: Statistical Mechanics and Its Applications335(1): 240–248.10.1016/j.physa.2003.12.014
  8. Choromanski, K., Matuszak, M. and Miekisz, J. (2013). Scale-free graph with preferential attachment and evolving internal vertex structure, Journal of Statistical Physics151(6): 1175–1183.10.1007/s10955-013-0749-1
  9. Dorogovtsev, S.N., Mendes, J.F.F. and Samukhin, A.N. (2000). Structure of growing networks with preferential linking, Physical Review Letters85(21): 4633–4636.10.1103/PhysRevLett.85.463311082614
  10. Gogoi, P., Bhattacharyya, D., Borah, B. and Kalita, J.K. (2011). A survey of outlier detection methods in network anomaly identification, The Computer Journal54(4): 570–588.10.1093/comjnl/bxr026
  11. Hirose, S., Yamanishi, K., Nakata, T. and Fujimaki, R. (2009). Network anomaly detection based on eigen equation compression, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 1185–1194.10.1145/1557019.1557147
  12. Host-Madsen, A. and Zhang, J. (2018). Coding of graphs with application to graph anomaly detection, 2018 IEEE International Symposium on Information Theory (ISIT), Vail, CO, USA, pp. 1829–1833.10.1109/ISIT.2018.8437551
  13. Jackson, M.O. and Rogers, B.W. (2007). Meeting strangers and friends of friends: How random are social networks?, American Economic Review97(3): 890–915.10.1257/aer.97.3.890
  14. Khalil, H. (2001). Nonlinear Systems, 3rd Edn., Pearson, Upper Saddle River, NJ.
  15. Koutra, D., Shah, N., Vogelstein, J.T., Gallagher, B. and Faloutsos, C. (2016). DELTACON: Principled massive-graph similarity function with attribution, ACM Transactions on Knowledge Discovery Data10(3): 28:1–28:43.10.1145/2824443
  16. Kudělka, M., Zehnalová, Š., Horák, Z., Krömer, P. and Snášel, V. (2015). Local dependency in networks, International Journal of Applied Mathematics and Computer Science25(2): 281–293, DOI: 10.1515/amcs-2015-0022.10.1515/amcs-2015-0022
  17. Lee, C.-Y. (2006). Correlations among centrality measures in complex networks, arXiv: 0605220.
  18. Moriano, P. and Finke, J. (2012). Power-law weighted networks from local attachments, Europhysics Letters99(1): 18002.10.1209/0295-5075/99/18002
  19. Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and Samatova, N.F. (2015). Anomaly detection in dynamic networks: A survey, WIREs Computational Statistics7(3): 223–247.10.1002/wics.1347
  20. Ruiz, D. and Finke, J. (2013). Invalidation of dynamic network models, Proceedings of the American Control Conference, Washington, DC, USA, pp. 138–143.10.1109/ACC.2013.6579827
  21. Savage, D., Zhang, X., Yu, X., Chou, P. and Wang, Q. (2014). Anomaly detection in online social networks, Social Networks39(C): 62–70.10.1016/j.socnet.2014.05.002
  22. Segarra, S. and Ribeiro, A. (2016). Stability and continuity of centrality measures in weighted graphs, IEEE Transactions on Signal Processing64(3): 543–555.10.1109/TSP.2015.2486740
  23. Shao, Z.-G., Zou, X.-W., Tan, Z.-J. and Jin, Z.-Z. (2006). Growing networks with mixed attachment mechanisms, Journal of Physics A: Mathematical and General39(9): 2035.10.1088/0305-4470/39/9/004
  24. Shoubridge, P., Kraetzl, M., Wallis, W.D. and Bunke, H. (2002). Detection of abnormal change in a time series of graphs, Journal of Interconnection Networks3(01n02): 85–101.10.1142/S0219265902000562
  25. Tong, J., Hou, Z., Zhang, Z. and Kong, X. (2009). Degree correlations in the group preferential model, Journal of Physics A: Mathematical and Theoretical42(27): 275002.10.1088/1751-8113/42/27/275002
  26. Valente, T.W., Coronges, K., Lakon, C. and Costenbader, E. (2008). How correlated are network centrality measures?, Connections28(1): 16–26.
  27. Yu, R., Qiu, H., Wen, Z., Lin, C.-Y. and Liu, Y. (2016). A survey on social media anomaly detection, SIGKDD Explorations18(1): 1–14.10.1145/2980765.2980767
DOI: https://doi.org/10.2478/amcs-2019-0027 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 363 - 373
Submitted on: Jun 1, 2018
Accepted on: Jan 18, 2019
Published on: Jul 4, 2019
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Diego Ruiz, Jorge Finke, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.