## 2 Related Work
_Circle Packing_[17], _BeamTree_[18], and _TreeMap_[19] are examples of containment-based representations while _Sunburst_[13] and _Iclicle Plots_[20; 21] are examples of adjacency-based representations. Moreover, hybrid techniques can be found such as _Elastic Hierarchies_[22], _Dendrogram_[23], or _Stacked Trees_[24]. Finally, Treevis.net [25] provides a classification of a wide range of tree representations depending on the features discussed above, such as explicit vs. implicit, dimensionality, or node alignment. For the specific visualization of compound graphs, some previous works are based on using a circle-packing representation for the hierarchy combined with a node-link diagram (_TugGraph_[26] and _GrouseFocks_[27]). Other authors proposed to use a Treemap-based representation for the hierarchy combined with a node-link diagram (_ArcTree_[28] and _NFlowVis_[29]). Additionally, other relevant approaches are proposed in _TreeNetViz_[30], combining a Sunburst representation with node-link diagram, and _MatLink_[12], that applies a node-link diagram for the hierarchy in combination with an adjacency matrix for the graph. All these approaches use a tree representation as the layout substrate, while [PERSON] et al. propose using a node-line diagram with a force-directed layout and representing the hierarchy by creating nested groups [31]. On the other hand, when data are complex, it is also necessary to provide mechanisms that allow the analyst to establish a dialogue with the data, and this can be achieved through interactivity [32]. In this way, the user, through manipulation of the visual representations, can build up an analytical discourse that can potentially lead to valuable new knowledge, confirm hypotheses previously established, or share findings and communicate them for effective decision-making [33]. In the literature, different classifications for the techniques for visual interaction can be found. For example, [PERSON] et al. suggest seven categories of interaction techniques: select, explore, reconfigure, encode, abstract/elaborate, filter, and connect [34]. Furthermore, [PERSON] divides the spectrum into five groups: dynamic projections, filtering, zooming, distortion, and linking and brushing [35]. As for visual editing, [PERSON]'s work is noteworthy, although it does not focus on relational data, but proposes an extension of interactive manipulation techniques for the direct visual editing of complex datasets [36]. With respect to editing a network, this process may involve either editing the attributes of nodes and relationships, or editing the existing nodes and relationships themselves. For the first case, [PERSON] et al. propose to use node-link diagrams paired with attribute-dependent layouts [37]. For example, they use a scatterplot for placing the nodes of the node-link diagram, and with drag-and-drop gestures, the values of the attributes would be visually edited. Regarding the editing of nodes and relationships, there are some systems designed for editing the network through a table while updating the visual representation of the graph on the fly (for example, _Cytoscape_[38] or _Gephi_[39]), but this approach is not strictly visual-based. The visual-based approach most commonly used is editing the graph directly through its node-link representation (forexample, _yEd Graph Editor_ ([[https://www.yworks.com/products/yed](https://www.yworks.com/products/yed)]([https://www.yworks.com/products/yed](https://www.yworks.com/products/yed)), accessed on 10 June 2022) or _Visual Paradigm_ ([[https://www.visual-paradigm.com/](https://www.visual-paradigm.com/)]([https://www.visual-paradigm.com/](https://www.visual-paradigm.com/)), accessed on 10 June 2022)). In this case, all of them use a similar approach for creating nodes, based on selecting a template and dragging it to the canvas or clicking on the location where the user would like the node to be placed.