# Multi-NETVIS: Visual Analytics for Multivariate Network
## 6 System Evaluation
### Results  
The evaluation process was as follows: (1) introducing data information and analysis tasks to the participants, as shown in Table 3; (2) introducing the basic usage methods and functions of Gephi and Multi-NetVis for the participants; (3) randomly dividing the sixteen  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Number** \\ \hline A1 & Supports completing analytical tasks quickly and accurately \\ A2 & Supports quickly locating the target node or local structure \\ A3 & Instructively supports analysis of the network structure, community structure, \\  & and local structure \\ A4 & Renders topology associated with attributes \\ A5 & Friendly interaction design reduces interference with analysis caused by \\  & edge/node occlusion \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assessment Items.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Analysis Task Description** \\ \hline T1 & Find out which brain area “group” attribute is “group1”, and use \\  & analysis to find the more critical brain areas within these brain areas (R2) \\ T2 & Identify the community of the most connected brain areas, and identify \\  & the most important brain area within that community (R1) \\ T3 & Identify two or three brain areas considered important in “group2” and \\  & evaluate their global, community, and local importance (R2, R3) \\ T4  
participants into two groups to complete the network analysis tasks using Multi-NetVis and Gephi; (4) after completing their analytical tasks, participants rated the system according to each description in Table 4. A five-level Likert scale was used to score points, with scores ranging from 1 to 5 representing \"strongly disagree\", \"disagree\", \"not sure\", \"agree\" and \"strongly agree\", respectively.  
The evaluation results (Figure 12) show that both Multi-NetVis and Gephi perform well in the analysis task completion (A1) and fast positioning analysis goals (A2), with Multi-NetVis having an advantage on A1. According to user feedback, Multi-NetVis provides a multi-granular topology view that facilitates analysis from a variety of perspectives. On A2, the analysis process and method designed in this paper require a \"global to local\" interaction process, while the interaction provided by Gephi is more direct. For the small-scale network data used in the evaluation experiment, Gephi is more advantageous. However, it is necessary to provide gradual exploration guidance with increases in scale. As a result, Multi-NetVis has a clear advantage in global, community, and local analysis (A3). According to the network layout scheme, the network attributes are represented by the topology structure, which helps provide association analysis of the topology structure and attributes (A4). However, the interaction aspect (A5) is slightly inadequate. Gephi provides ample search filtering and statistical methods for the attributes. Therefore, it is more conducive to quickly finding objects with specified attributes and analyzing the crucial nodes in the network from various perspectives.  
Based on this user feedback and the evaluation results, Multi-NetVis has two main advantages. On one hand, association analysis and the comparative analysis of different dimensional layouts make analysis more efficient and effective and provide more information than a one-dimensional spatial layout. On the other hand, a progressive analytical process is more clear. Nevertheless, the method is relatively weak in terms of diversified interactions, and we intend to continue to make further improvements in our future work. The multi-grained context of this process provides flexible community analysis and links between attributes and topology.