Network robustness is a common and fundamental property in many high-impact applications. This project aims to develop basic theories and algorithms to make a robust network by optimizing its underlying topology. The figure on the left presents an illustrative comparison. The existing observatory work takes a given network as the input (the blue network on the left) and outputs a specific robustness measure. This project aims to take such robustness measure as the additional input to intervene the current network topology (i.e., adding 6 red dashed lines). Contact: Hanghang Tong |

Optimal Connectivity on Big Graphs (at IEEE BigData 2015 and SDM 2016)

Applied Matrix Analytics (at ICDM 2013 and SDM 2013, with F. Wang and C. Ding)

Software(Edge Operation) (26 methods to optimize network connectivity)

Software(Node Operation) (10 methods to optimize network connectivity)

C. Chen, H. Tong, L. Xie, L. Ying and Q. He: Fascinate: Fast cross-layer dependency inference on multi-layered networks. KDD 2016 (Invited to TKDD SI on 'Best of KDD’16’)

X. Su, H. Caceres, H. Tong, and Q. He. Online travel mode identification using smartphones with battery saving considerations. IEEE Trans. on ITS 2016 (to appear)

C. Chen and H. Tong: Fast Eigen-Functions Tracking on Dynamic Graphs. SDM 2015 (Invited to SAM SI on ‘Best of SDM’15’)

L. Shi, H. Tong, J. Tang, C. Lin: Flow-Based Influence Graph Visual Summarization. ICDM 2014

H. Tong, B. Prakash, T. Eliassi-Rad, M. Faloutsos, C. Faloutsos. Gelling, and Melting, Large Graphs by Edge Manipulation. CIKM 2012 (best paper award)