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[93] A. Subramanya and P.P. Talukdar. Graph-based semi-supervised learning.
Synthesis Lectures on Artificial Intelligence and Machine Learning, 8(4):1–
125, 2014.
[94] P.P. Talukdar and K. Crammer. New regularized algorithms for transductive
learning. In Machine Learning and Knowledge Discovery in Databases, pages
442–457. Springer, 2009.
[95] R. Tamassia. Handbook of Graph Drawing and Visualization. CRC Press,
2013.
[96] W. Tang, Z. Lu, and I.S. Dhillon. Clustering with multiple graphs. In Proceedings of the 9th IEEE International Conference on Data Mining, pages
1016–1021. IEEE, 2009.
[97] H. Tao, C. Hou, and D. Yi. Multiple-view spectral embedded clustering using a co-training approach. In Computer Engineering and Networking, pages
979–987. Springer, 2014.
[98] V.A. Traag and J. Bruggeman. Community detection in networks with positive
and negative links. Physical Review E, 80(3):036115, 2009.
[99] J. Travers and S. Milgram. An experimental study of the small world problem.
Sociometry, pages 425–443, 1969.
[100] G. Trenkler. Handbook of Matrices. Computational Statistics & Data Analysis, 25(2):243–243, July 1997.
[101] U. Von Luxburg. A tutorial on spectral clustering. Statistics and Computing,
17(4):395–416, December 2007.
[102] U. Von Luxburg, M. Belkin, and O. Bousquet. Consistency of spectral clustering. The Annals of Statistics, pages 555–586, 2008.
[103] U. Von Luxburg, O. Bousquet, and M. Belkin. On the convergence of spectral
clustering on random samples: the normalized case. Learning Theory, pages
457–471, 2004.
[104] U. Von Luxburg, O. Bousquet, and M. Belkin. Limits of spectral clustering. Advances in Neural Information Processing Systems (NIPS), 17:857–864,
2005.
[105] D. Wagner and F. Wagner. Between min cut and graph bisection. In Proceedings of the 18th International Symposium on Mathematical Foundations
of Computer Science, pages 744–750, London, UK, 1993. Springer-Verlag.