Network-Reconstruction-Resources

Given a network of N nodes and each time series measurement is x_{i}(t) for i = 1, 2, ..., N, we would like to infer the structual connectivity between all pair of nodes.

Covariance Approach

1. Noise Bridges Dynamical Correlation and Topology in Coupled Oscillator Networks
   Jie Ren, Wen-Xu Wang, Baowen Li, and Ying-Cheng Lai Phys. Rev. Lett. 104, 058701 – Published 4 February 2010

2. Extracting connectivity from dynamics of networks with uniform bidirectional coupling
   Emily S. C. Ching, Pik-Yin Lai, and C. Y. Leung Phys. Rev. E 88, 042817 – Published 25 October 2013

3. Solving the inverse problem of noise-driven dynamic networks
   Zhaoyang Zhang, Zhigang Zheng, Haijing Niu, Yuanyuan Mi, Si Wu, and Gang Hu Phys. Rev. E 91, 012814 – Published 21 January 2015

4. Reconstructing weighted networks from dynamics
   Emily S. C. Ching, Pik-Yin Lai, and C. Y. Leung Phys. Rev. E 91, 030801(R) – Published 24 March 2015

5. Reconstructing links in directed networks from noisy dynamics
   Emily S. C. Ching and H. C. Tam Phys. Rev. E 95, 010301(R) – Published 5 January 2017

6. Reconstructing network topology and coupling strengths in directed networks of discrete-time dynamics
   Pik-Yin Lai Phys. Rev. E 95, 022311 – Published 24 February 2017

7. Reconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations
   Yang Chen, Zhaoyang Zhang, Tianyu Chen, Shihong Wang & Gang Hu Scientific Reports volume 7, Article number: 44639 (2017) Published: 21 March 2017

8. Reconstructing networks from dynamics with correlated noise
   H.C. Tam, Emily S.C. Ching, Pik-Yin Lai, Physica A: Statistical Mechanics and its Applications Volume 502, 15 July 2018, Pages 106-122

Compressed Sensing Approach

1. Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing
   Wen-Xu Wang, Ying-Cheng Lai, Celso Grebogi, and Jieping Ye Phys. Rev. X 1, 021021 – Published 21 December 2011

2. Revealing physical interaction networks from statistics of collective dynamics
   Mor Nitzan, Jose Casadiego, and Marc Timme, Science Advances 10 Feb 2017: Vol. 3, no. 2, e1600396

3. Model-free inference of direct network interactions from nonlinear collective dynamics
   Jose Casadiego, Mor Nitzan, Sarah Hallerberg & Marc Timme Nature Communications volume 8, Article number: 2192 (2017) Published: 19 December 2017

Inverse Ising Approach

1. Financial interaction networks inferred from traded volumes
   Hong-Li Zeng, Rémi Lemoy, and Mikko Alava, Journal of Statistical Mechanics: Theory and Experiment, Published 11 July 2014 • © 2014 IOP Publishing Ltd and SISSA Medialab srl

Specialised Topic: Hidden Nodes

1. Effects of hidden nodes on network structure inference
   Haiping Huang, IOP Science Journal of Physics A: Mathematical and Theoretical, Published 11 August 2015 • © 2015 IOP Publishing Ltd

2. Data-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodes
   Ri-Qi Su, Wen-Xu Wang, Xiao Wang and Ying-Cheng Lai, Royal Society Open Science, Published: 01 January 2016

3. Inference of targeted interactions of networks with data of driving and driven nodes only by applying fast-varying noise signals
   Chaoyang Zhang, Yang Chen, Gang Hu, Physics Letters A Volume 381, Issue 31, 21 August 2017, Pages 2502-2509

4. Effects of hidden nodes on the reconstruction of bidirectional networks
   Emily S. C. Ching and P. H. Tam Phys. Rev. E 98, 062318 – Published 21 December 2018

5. Detecting Hidden Units and Network Size from Perceptible Dynamics
   Hauke Haehne, Jose Casadiego, Joachim Peinke, and Marc Timme Phys. Rev. Lett. 122, 158301 – Published 16 April 2019

6. Reconstruction of dynamic networks with time-delayed interactions in the presence of fast-varying noises
   Zhaoyang Zhang, Yang Chen, Yuanyuan Mi, and Gang Hu Phys. Rev. E 99, 042311 – Published 30 April 2019