# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 6 Discussion  
In this paper, we propose a method to visualize the layout of the graphs considering the attribute information of nodes. Compared with the traditional graph visualization layout method, the method in this paper can reflect the attribute information of the community graph data well. As in the above example, the clustering in the graph layout result can intuitively reflect the attribute information implicit in each attribute of the graph data. In the layout method that does not consider attributes, some nodes may be far away from most nodes with the same attributes due to the structural connection relationship. Although the layout uses colors and shapes for the logo, it still gives people a mixed feeling. On the contrary, the introduction of additional powerful constraints through attribute similarity can enable people to better confirm the attribute information of nodes.  
In our method, attribute community force is applied to nodes to gather nodes with the same attribute into the same area. For some nodes, attribute constraints cause them to move away from neighboring nodes with different attributes, which creates additional edge crossings in the layout. This makes it difficult for users to explore the larger attribute community. On the one hand, we strengthen users' exploration ability through interaction design in the system. On the other hand, by defining community force strength, nodes closely connected with other attributes are more likely to be distributed on the periphery of the community to reduce crossover.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Ours** & **FR** & **linLog** & **FA2** \\ \hline
**task1** & **4.6** & 2.2 & 2.6 & 3.2 \\
**task2** & **3.8** & 1.6 & 2 & 1.5 \\
**task3** & **3.4** & 2.8 & 2 & 2.4 \\ \hline \end{tabular}
\end{table}
Table 6: Average scores of users study.  
Our method automatically calculates the location of the attribute communities in space, allowing for a flexible layout. The idea of tree layout can be used in future work. Predetermine the location of each community layout based on the distribution of leaf nodes in the tree. For some nodes in the network, it may be connected to a large number of edges. We can also borrow PLANET's [37] idea of using polar coordinates to increase angles between different edges and enhance resolution.  
Our method allows users to visualize the graph layout by selecting the node attributes they are interested in, to explore the attribute information implied by the graph data under this attribute. This kind of user interest-driven model makes the method in this paper flexible. If the node contains a non-numeric attribute, then each attribute value under the attribute is regarded as a kind of attribute community. If the node contains numerical attributes, users can discretize the attribute values according to their preferences, and each interval is an attribute community.  
Adjusting the strength of attribute constraints has a significant impact on the results of the layout. When the attribute constraints are too tight, the nodes will closely surround the community center, resulting in a large number of overlapping nodes within the community. It is unable to distinguish the connection relationship between the same community node. When the attribute constraints are too loose, although the structural information between the same community nodes can be displayed well, the degree of discrimination between attribute communities will decrease.