# Multi-NETVIS: Visual Analytics for Multivariate Network
## 5 System Implementation and Case Studies
### Case Studies
#### 5.2.1 Brain Network Analysis of AD Patients  
We extracted brain activity data from a database consisting of healthy people and AD patients, selecting a threshold of 0.2 to extract brain network data [23] and obtaining a final network containing 90 nodes and 811 edges. The nodes retained \"He Yong\" partitioning as an attribute. The node attribute groups 1-5 were \"Auditory and language/visuospatial\", \"Emotion/decision-making\", \"Social/visual\", \"Control of action\", and \"Memory and learning/visual\", respectively. In this case, we compared and analyzed the brain network of AD patients to explore the brain areas of AD patients with abnormal connections.  
Overall network-based analysis: Based on the overall network shown in Figure 7, it can be seen that the AD network is sparse and uneven. Certain areas are too sparse or too dense, and the difference in the betweenness centrality distribution range of the AD and HC groups is relatively large. As for attribute distribution, there are numerous brain areas in the AD network that appear to be low, indicating that these brain areas lack connections with other areas. The closeness centrality index of a few brain areas is much higher than in healthy people, allowing us to infer increased connectivity in certain brain regions.  
Community-based analysis: Switching the 3D layout view to a community-based layout, we can set the color of nodes by \"group\" attribute, as shown in Figure 8. The AD network is divided into nine communities, while the HC network has only four communities. It can be inferred that certain brain areas have lost many connections with other brain areas and become separated into small communities. From the AD graph, it can be seen that the nodes in these communities possess a small scale and sparse connections. Thus, we select such communities in the AD network for integration and further analyze them in the 2.5D layout.  
Local-based analysis: Using the 2.5D layout of the merged multiple community networks, as shown in Figure 9, we take the \"group\" as the layering condition, and as layer four is the brain area related to memory, we view the 2D layout of this layer. It can be observed that the connectivity of the hippocampus is relatively low. Selecting this node in the 2.5D layout, we find that this brain area is only correlated with a few nodes in layers 0 and 2. Additionally, the degree of this node is only 11, while its degree in the normal brain network is 18. Through common analysis we can obtain other brain areas with weakened correlations, such as the amygdala and insula.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Name** & **Declaration** \\ \hline D1 & Brain network & The dataset has two part (Alzheimer’s disease patients and healthy individuals). \\ D2 & Book Correlation network & The dataset consists of American political books sold on the Amazon.com platform. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data Declaration.  
Figure 7: The overall topology and central distribution; the node colors indicate different communities.