1School of Mathematical Science, Anhui University, Hefei 230601, China
2State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain & Research, School of Systems Science, Beijing Normal University, Beijing 100875, China
Fig. 2. Reconstructing of node 4 in the Karate network based on compressive sensing framework (the reconstruction method is introduced in Subsec. 2.4).
Fig. 3. Driving-response experiments. System is shifted from one stable state (the stable state is a fixed point (a), or a periodical trajectory (b)) to another position by input a driving signal I. The difference of the trajectories contains information about the topology.
Fig. 4. Reconstructing the neighbors of node 33 in Karate network: (a) The real structure of the Karate network; (b) the binary state data; (c) inferring the neighbors of node 33 based on EM algorithm; (d) the real neighbors of node 33.