## 3 Results and Discussion
### Network Visual Mining  
#### 3.2.1 Visual Mining of HSPNs; The Most Central and Atypical ABFPs  
In addition to the numerical characterization of HSPNs by the calculation of network parameters, their visualization also provides new and simple insights to unravel the complex relationships of the objects they represent. In our case, the HSPNs were used to represent and analyze the chemical space of 174 non-redundant ABFPs by applying AF similarity networks with the application of the half-proximal space graphs [34; 36; 37]. Network visualization can mirror several network parameters such as density and communities; node size can be ranked according to different centrality measures, e.g., node degree, harmonic, hub-bridge, betweenness, etc. Thus, the most important or central peptides can be highlighted, and the edges weighted with high or low similarities. This work aimed at the outset to exploit the visual representation of complex networks representing ABFPs in order to analyze their structural space and associated metadata, both of which are relevant for the discovery and design of antibiofilm agents [31]. Both the original HSPN (no cutoff) and the HSPN model (cutoff at 0.65) representing the structural space occupied by the 174 ABFPs are depicted in Figure 4, and the Supplementary File S2 complements the network projection through numerical characterization. Networks communities are highlighted with different colors, and nodes' importance is represented by the node degree centrality. The original HSPN shows 5 communities clearly identified by different colors, while in the HSPN model bearing 30 communities (20 singletons included), their delineation by color is more difficult (see details in Supplementary File S2). However, since the HSPN model is a low-density network with a smaller number of edges (325) than the original HSPN (689), it allows for a better depiction of the nodes' relationships (Supplementary File S2). The most relevant nodes according to the node degree were clearly identified in the HSPN with no cutoff (e.g., starPep_07526, starPep_02281, starPep_00048, starPep_03668, starPep_08958), whereas upon applying the similarity cutoff, a significant fraction of graph  
Figure 4: (**A**) HSPNs’ visualization using the Fruchterman–Reingold layout, without similarity cutoff and (**B**) with a similarity cutoff of 0.65. Peptides communities are represented by different colors, while the nodes’ size was scaled according to node degree. Figure 3: Degree distribution of the HSPNs corresponding to the 174 ABFPs with no similarity cutoff and after applying a cutoff of 0.65.  
edges are lost and the nodes' degree decreases. This is why only two of the most connected nodes were highlighted (the starPep_00048 and starPep_03668). As these two peptides were also brought up in the original HSPN in similar locations and clusters, it can be deduced that changes in the HSPN topology upon applying similarity cutoffs do not alter the most popular peptides in both networks. In order to address this question, the top ten most relevant peptides according to each four centrality measures (node degree, harmonic, betweenness and hub-bridge) were extracted from the HSPNs with and without similarity cutoff (Supplementary File S2). Then, the intersection of the resulting four peptide sets was analyzed for each HSPN. Table 1 displays the common peptides identified from the ten top-ranked ABFPs by four, three and two centrality measures, from HSPNs with and without similarity cutoff. The last four rows of each HSPN represent singular peptides identified for each of the four centralities. The cluster/community containing the ten top-ranked ABFPs is also displayed. Table 1 shows a different composition of common and singular peptides from the ten top-ranked ABFPs by centrality measures at HSPNs with and without similarity cutoff.