# Multi-NETVIS: Visual Analytics for Multivariate Network
## 4 Research Methods  
Based on the aforementioned requirements, we designed a workflow consisting of four parts (Figure 1). Data Processing: data preprocessing is performed, and the data attributes are extracted during data processing. The data are constructed as network data with nodes and edge objects. Network Analysis: network analysis mainly includes centrality analysis, community mining, and attribute-based analysis. Community mining focuses on the choice of division of two adjacent network communities, and includes network division based on its attributes and the community mining algorithm. Visual Mapping: visual mapping visualizes the original information and structure of the network from its attributes and topological structure, including the community exploration module and the network topological module. The community exploration module includes the analysis of community centrality and community topology. The network layout module completes different network layouts, including the 3D layout showing the global network structure, the 2.5D layout showing the associated structure of the network community, and the 2D layout showing the local structure of the network. Interactive Exploration: the interactive exploration module combines the three kinds of layouts to provide multi-granularity topology contexts. Users can complete the whole exploration process according to multiple interaction modes such as selection and browsing, beginning with the global overview, which gradually reduces exploration granularity, and finally focusing on local nodes or edges. Meanwhile, multiple topology contexts can be compared and correlated during analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Task** \\ \hline
1 & Finding entities and properties in the multivariate network \\
2 & Calculating derived properties which are not originally presented in the data \\
3 & Revisiting entities and properties already identified or determined \\
4 & Deriving insight or knowledge from the data as an outcome of performing a \\  & sequence of operational and analytical tasks \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic tasks in analysis of multivariate networks.  
In order to consider both the attributes and the topological structure, we represent the network attributes from the perspective of topological layout and design the layout scheme with the characteristics of the 2D layout, the 2.5D layout, and the 3D layout. The same network presents different layout effects in different dimensional layouts, which makes it difficult to lock and analyze the target. Therefore, we propose a multidimensional space layout transformation method for layout calculation in order to maintain the relative position of the topological context layouts in multiple dimensions.  
The 3D space provides infinite space and a 360\({}^{\circ}\) observation angle, which is suitable for presenting the large-scale network structure. In this study, the 3D layout is selected to present the overall network structure, allowing users to master the global topology of the network. The 2.5D layout of nodes depends on the network attribute value, which can be mapped directly from the topology. Here, we adopt a hierarchical layout model to implement the 2.5D layout and introduce an edge bundling algorithm to improve the visual effects of the network layout. Due to the limitation of 2D space and the simplicity of interaction, it is more suitable for presenting the local details of the network (R2). Therefore, the 2D layout is adopted to present the small-scale local network structure, with a 2D force-directed layout and a circular layout.