\subsection*{Gephi}
Gephi \cite{gephi_site} is a comprehensive tool created to visualize, analyze, filter, and manipulate network graphs, taking into account both qualitative and quantitative variables present in the data. With Gephi, users have the ability to employ predefined layout algorithms to automatically arrange nodes or manually position them as desired. Gephi utilizes advanced layout algorithms to determine the shape and arrangement of graphs. These state-of-the-art algorithms, designed for optimal efficiency and quality, are integrated within the software. The Layout palette within Gephi empowers users to modify layout settings in real-time, significantly enhancing user feedback and overall experience during the graph visualization process.

Furthermore, the software offers the capability to compute network metrics, especially for  social network analysis (SNA) and scale-free networks, empowering users to leverage these analyses results to tailor the visual representation of their network graphs and facilitates the creation of high-quality vector graphics for network visualizations, ensuring their suitability for publication purposes. Additionally, the tool enables the exportation of filtered and analyzed networks in data formats suitable for interactive online display, such as PDF, PNG and SVG, employing JavaScript visualization libraries. The tool can read the majority of graph file formats but also supports CSV, relational databases import and spreadsheets, although the preferred format input file are surely GEXF and GraphML, two variants of XML used for storing and exchanging network graph data.

\begin{figure}[h]
    \centering
    \includegraphics[scale=.3]{Figures/chapter4/Gephi/test.png}
    \caption{Gephi Fruchterman Reingold layout example }
    \label{fig:gephi}
\end{figure}

Also in this case, given the generality of the tool, the example shown in the figure (figure \ref{fig:gephi}) uses as input a fictitious dataset representing two subnets and the respective nodes and messages exchanged. In this particular case the algorithm chosen for the representation of the layout is the Fruchterman Reingold \cite{frucht_paper}. This algorithm is a force-directed layout algorithm used to visually arrange nodes in a graph. It treats nodes as steel rings and connects them with springs representing edges. Attractive forces act like springs, while repulsive forces resemble electrical forces. The algorithm aims to minimize the system's energy by adjusting node positions and altering the forces between them. For further information, please refer to the Force Directed algorithm. After selecting the algorithm and waiting for the stabilization of the graph in the application, slight aesthetic changes were made such as the size of the nodes and the color of the networks, to allow a better view of the exported image.

As depicted in the image, the tool is particularly suitable for representing relational data, even with high density of connections, and one of its main features is the speed at which it calculates these large networks. In addition to its basic functionality, the availability of a wide range of freely downloadable plugins extends the already extensive capabilities of the tool.The tool is freely available for download on its official website for all major operating systems. However, its broad interoperability does not make it a specific tool for the cybersecurity context. Furthermore, in this specific case, exporting the graph as an image format loses the interactivity that could characterize a functional view for an analyst.

