MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication
Fig 3
Synthetic multilayer network evaluation.
In both the panels, plots on the left and right are respectively obtained using Synthetic Multilayer Network Model 1 and 2. (A) As more nodes from source set 2 become part of the ground truth (shown as increasing fraction x), our MultiCens query-set centrality (QC) outperforms the existing methods and other MultiCens measures (local and global centrality, denoted LC and GC respectively) to a larger extent, especially in the presence of extra communities in the query-set layer (right). We calculated inter-layer degree and versatility using inter-layer connections to the query-set only; and let RWR-H’s seed nodes be same as the query-set. Each plot shows the connection strength (x-axis) against the number of ground truth nodes in the top 100 ranked nodes (y-axis). (B) Analysis of ranks based on MultiCens QC and our closely related method RWR-H. MultiCens QC (y-axis) distinguishes nodes coming from different sets somewhat better than RWR-H (x-axis), with this trend more clear in Synthetic Multilayer Network Model 2 than 1. Both these plots correspond to connection strength 1 as shown in (A).