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Networks appear naturally in the above applications, and in many more. Often such networks are collected from different sources, at different times, at different granularities; thus creating a disparity issue. The overall goal of this project is to discover correspondence in disparate networks, to enable collective mining of them, including three thrusts: (1) network-level correspondence, (2) node-level correspondence, and (3) real-world applications. Contact: Hanghang Tong |
Haichao Yu (master 2018, now at Intuit)
Scott Freitas (master 2018, now Ph.D student at Gatech)
Si Zhang (Ph.D student)
Boxin Du (Ph.D student)
B. Du and H. Tong FASTEN: Fast Sylvester Equation Solver for Graph Mining. KDD2018
B. Du, S. Zhang, N. Cao and H. Tong: FIRST: Fast Interactive Attributed Subgraph Matching. KDD 2017
D. Koutra, H. Tong and D. Lubensky: BIG-Align: Fast Bipartite Graph Alignment. ICDM 2013: 389-398
J. Ni, H. Tong, W. Fan, and X. Zhang: Inside the atoms: ranking on a network of networks. KDD 2014
S. Zhang, H. Tong, J. Tang, J. Xu, W. Fan. iNEAT: Incomplete Network Alignment. ICDM 2017