References

1. H. Bunke, Graph matching: theoretical foundations, algorithms, and applications, in Proceedings of the International Conference on Vision Interface, pp. 82–88, May 2000.

2. V. Kann, On the approximability of the maximum common subgraph problem, in Proceedings of 9th Annual Symposium on Theoretical Aspects of Computer Science, pp. 377–388, 1992.

3. A.J. Seary, W.D. Richards, Spectral methods for analyzing and visualizing networks: an introduction, in Dynamic Social Network Modeling and Analysis, National Academic Press, pp. 209–228, 2003.

4. B. Nadler, S. Lafon, R. Coifman, I. Kevrekidis, Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators, in Neural Information Processing Systems (NIPS), 2005.

5. A. Ng, M. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, in Advances in Neural Information Processing Systems 14 (T. Dietterich, S. Becker, Z. Ghahramani, ed.), MIT Press, 2002.

6. A.G. Thomason, Pseudo-random graphs, Random Graphs ’85, North-Holland Mathematical Study, vol. 144, pp. 307–331, 1987.

7. F.R.K. Chung, R.L. Graham, R.M. Wilson, Quasi-random graphs, Combinatorica, 9(4), 345–362 (1989).

8. F.R.K. Chung, Spectral Graph Theory (CBMS Regional Conference Series in Mathematics), American Mathematical Society, 1997.

9. X. Wang, D. Loguinov, Wealth-based evolution model for the internet as-level topology, in Proceedings of IEEE INFOCOM, April 2006.

10. D. Fay, H. Haddadi, A.G. Thomason, A.W. Moore, R. Mortier, A. Jamakovic, ...

Get Statistical and Machine Learning Approaches for Network Analysis now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.