Fig. 2

SCS-learn and SCS-consensus evaluations for ANDES benchmark network [223 nodes, 338 edges, 〈k〉=3.03]. Precision, Recall and F-score results for an increasing proportion of eigenvectors (up to 40 %), subgraphs of 12 nodes (5 % variables) and 150 samples. Scores take misorientations into account. Each point is an average over 5 datasets (results for different subgraph and dataset sizes follow a similar trend, see Additional file 1). (SCS-learn, top three rows) Three learning algorithms are embedded to reconstruct a network from subgraphs whose vertices are selected from the magnitude of eigenvector elements (SCS-learn, red solid line), spectral fuzzy C-means partitioning (light blue solid line), spectral K-means clustering (dark blue solid line), random subsets (green solid line) and recursive bi-partitioning (salmon solid line). Results are compared to scores obtained without spectral or partitioning embedding (red dashed line). (SCS-consensus, bottom row) The SCS-learn reconstructions are combined in a consensus network (red solid line) and compared with individual SCS-learn outcomes (gray dashed lines). Scores are computed from the top 338 consensus edges (results for different number of consensus edges follow a similar trend, see Additional file 1)