Publications

13. Yan, X., Sadler, B. M., Drost, R. J., Yu, P. L. & Lerman, K. Graph Filters and the
Z-Laplacian. IEEE Journal of Selected Topics in Signal Processing 11, 774–784 (2017).http://ieeexplore.ieee.org/document/7986982/
12. Yan, X., Teng, S.-H. & Lerman, K. Multi-layer Network Composition Under a Unified
Dynamical Process. in Social, Cultural, and Behavioral Modeling: 10th International Conference, SBP-BRiMS 2017, Washington, DC, USA, July 5-8, 2017, Proceedings 315–321 (Springer International Publishing, 2017). doi:10.1007/978-3-319-60240-0_38
11.
Merkurjev, E., Bertozzi, A., Yan, X. & Lerman, K. Modified Cheeger and ratio cut methods using the Ginzburg–Landau functional for classification of high-dimensional data. Inverse Problems 33, 074003 (2017).
10.
Yan, X. Bayesian Model Selection for Stochastic Block Models. In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 323–328 https://arxiv.org/abs/1605.07057
9.
Yan,X., Teng S., Lerman K., Ghosh R. Capturing the interplay of dynamics and  networks through parameterizations of Laplacian operators. https://peerj.com/articles/cs-57/
8.
Lerman, K., Yan, X. & Wu, X.-Z. The Majority Illusion in Social Networks. PLoS One (2016). http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147617
7.
Daianu, M. et al. Information-theoretic Characterization of Neuroimaging Derived Metrics for Cognitive Decline in the Elderly. (2015).
6.
Gupta, S., Yan, X. & Lerman, K. Structural Properties of Ego Networks. in Social Computing, Behavioral-Cultural Modeling, and Prediction (eds. Agarwal, N., Xu, K. & Osgood, N.) 9021, 55–64 (Springer International Publishing, 2015). https://arxiv.org/pdf/1411.6061.pdf
5.
Ghosh, R., Teng, S., Lerman, K. & Yan, X. The Interplay Between Dynamics and Networks: Centrality, Communities, and Cheeger Inequality. in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1406–1415 (ACM, 2014). http://doi.acm.org/10.1145/2623330.2623738
4.
Zhu, Y., Yan, X. & Moore, C. Oriented and degree-generated block models: generating and inferring communities with inhomogeneous degree distributions. Journal of Complex Networks2, 1–18 (2014). http://comnet.oxfordjournals.org/content/2/1/1.abstract
3.
Yan, X. et al. Model selection for degree-corrected block models. Journal of Statistical Mechanics: Theory and Experiment2014, P05007 (2014). http://stacks.iop.org/1742-5468/2014/i=5/a=P05007
2.
Zhu, Y., Yan, X., Getoor, L. & Moore, C. Scalable Text and Link Analysis with Mixed-topic Link Models. in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 473–481 (ACM, 2013). http://doi.acm.org/10.1145/2487575.2487693
1.
Moore, C., Yan, X., Zhu, Y., Rouquier, J.-B. & Lane, T. Active Learning for Node Classification in Assortative and Disassortative Networks. in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 841–849 (ACM, 2011). http://doi.acm.org/10.1145/2020408.2020552

 

 

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