## 5 Conclusions  
This paper presents a framework for the visual exploration and editing of graphs by addressing the complexity derived from the high number of nodes and connections through different strategies. On the one hand, the organization of the data into levels of abstraction allows information to be aggregated, thereby reducing the visual and cognitive complexity perceived by the user. In addition to the ability to summarize information by means of aggregations, we also have included operations to selectively eliminate certain elements: filtering, hierarchy pruning, or focusing on a subtree allows discarding less relevant information to focus the exploration on a subset of elements. The possibility of adjusting visual properties such as transparency or the location and trajectory of the elements facilitates the reduction of visual cluttering, boosting the perception of the represented elements. These features together with the interaction capabilities provide an efficient environment for the visual exploration of complex compound graphs. The selected visual representation blends the hierarchical organization of nodes with their connectivity in an intuitive way. This hierarchical structure provides natural support for exploring the graph at different levels of abstraction, allowing the data to be visualized at an adaptive level of detail that provides more precision in areas of interest while showing a summary of information in other less relevant areas. This strategy facilitates the visualization of large graphs and helps to break down complex systems in order to analyze the interactions of their elements at different organizational levels. However, the aggregation of nodes into meta-nodes encapsulates details that would only be observable by uncollapsing the aggregated meta-node; this work proposes the enhancement of the representation by including visual metaphors that summarize properties of the collapsed (and therefore not visible) subtree to provide information to guide the user in the navigation process. The visual metaphor presented here is extremely simple, but a more elaborate design would allow a better characterization of the most relevant properties of the collapsed sub-tree, thereby providing a summarized outline of the hidden hierarchy without disaggregating the meta-node. Many areas generate data that have a hierarchical organization, such as biological systems, geographical data, or socio-economic data. However, even when the nodes do not present an intrinsic hierarchy, it is possible to apply techniques for their automatic hierarchical grouping, based on certain attributes of interest. This would allow applying the developed visualization environment to analyze complex graphs following a multiscale approach, even if they do not present a compound graph structure. No less interesting would be to perform different hierarchical groupings of the same data, according to different properties, which would allow interpreting the data from multiple perspectives derived from the restructuring of the hierarchy. The editing and creation capabilities turn this framework into a tool that aids the construction of graphs in a visual and interactive way, taking advantage of all the complexity management strategies already embedded in the viewer. This approach allows translatingthe mental model of a user by providing resources to build the representation following a top-down approach and to observe at the same time the hierarchical organization of the elements and their connections or dependencies. The results shown in this paper illustrate the applicability of the proposed approach to two different domains, demonstrating both the capabilities for the exploration of existing datasets and the visual creation of graphs that reflect the mental structure that a user has of a given domain. In both cases, Carbonic provides strategies for conveying information in a meaningful way. Regarding future work, the presented framework can evolve in multiple directions.