The k-core number of a node is an essential centrality metric in network analysis . However, performing k-core decomposition to the entire network is unrealistic in many real-world scenarios. This raises the need to estimate k-core numbers using only nodes’ local information . Transfer learning, enables us to learn knowledge from familiar domains, and transfer to other domains . We aim to use transfer learning, in order to learn a model from networks in which k-core numbers of nodes are known (or can be computed), and apply this model to estimate k-core numbers of nodes in other networks where performing k-core decomposition is unrealistic. In this thesis, the student reads and studies related literatures on k-core decomposition [1, 4], k-core number estimation  as well as transfer learning . The student implements a transfer learning algorithm which learns a model from (an) existing network(s) and estimates nodes’ k-core numbers in other networks without having the entire network as input. The student then applies the algorithm to real-word networks and evaluates its performance (accuracy and time complexity) with existing approaches.
Prerequisites: Python or Matlab/Octave, knowledge in network theory and machine learning.
 F. D. Malliaros, A. N. Papadopoulos, and M. Vazirgiannis, “Core decomposition in graphs: Concepts, algorithms and applications,” in EDBT, 2016, pp. 720–721.
 M. P. O’Brien and B. D. Sullivan, “Locally estimating core numbers,” in 2014 IEEE International Conference on Data Mining (ICDM), IEEE, 2014, pp. 460–469.
 S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345–1359, 2010.
 W. Khaouid, M. Barsky, V. Srinivasan, and A. Thomo, “K-core decomposition of large networks on a single PC,” Proceedings of the VLDB Endowment, vol. 9, no. 1, pp. 13–23, 2015.