## 5 Evaluations
### Experiments on Layout Approach  
#### 5.1.1 Experiment Settings  
**Datasets:** In terms of dataset selection, this study used the Newcomb Fraternity [46], McFarland [47], and email-Eu [48] datasets for comparative experiments, as shown in Table 1.  
The Newcomb dataset captures sociometric preference rankings among 17 students in a fraternity at the University of Michigan over a 15-week period. At the end of each week, these students ranked the other 16 students in order of preference from 1 to 16, with no duplicate rankings allowed. For this study, only the relationships representing the top three rankings each week were retained as edges in the network, providing insights into the changing relationships among the students. Consequently, a set of 15 networks was generated, each averaging 17 nodes and 40 edges. The [PERSON] dataset, sourced from [PERSON]'s research on classroom interactions, documents student interactions in a classroom setting to understand the learning process. It unveils the social processes involved in constructing, maintaining, and altering classroom order. The dataset records interactions among 20 students across 82 evolutionary steps. The email-Eu dataset comprises email data from a European company, with the senders and recipients being core members of the organization. The edges in this dataset are directed, signifying the sender (from) and receiver (to) of each email, along with the timestamp of email transmission. The number of events in this network does not vary a lot in each day, and it do not contain interval across days without any event. Considering this network is relatively stable and our layout method is not timeslicing sensitive, we choose to use a simple timeslicing technique, namely the uniformed timeslicing technique, to process the data. It is worthy to note that this processing technique may hide or lose patterns on other time scales. Further timeslicing sensitive research may refer to more effective techniques, such as the non-uniformed timeslicing [38; 49]. For this study, a continuous 30-day subset of data was selected, merging and processing the daily email records into an undirected network, resulting in a set of networks spanning 30 consecutive states. **Compared Methods:** we conducted comparative experiments using the following methods: FR algorithm, Incremental algorithm [17], and Aging algorithm [16]. The FR algorithm is the most widely applied network layout algorithm, which provides a baseline for good layout quality. The Incremental algorithm and the Aging algorithms are classical online dynamic network layout approaches. The incremental algorithm is used for incremental layout adjustments. The Aging algorithm introduces node age to limit node movement probabilities. **Stability and Quality Metrics:** For dynamic network layout algorithms, evaluation is typically conducted from two perspectives: stability and layout quality. In terms of stability metrics, a common approach is to measure the displacement \(\delta pos\) of nodes between adjacent layouts. When the network structure is updated, a smaller average displacement in the new layout indicates that there is less node movement between adjacent layouts, signifying greater stability and better maintaining the mental map. Some researchers also use layout energy as a metric, which is derived from relationships within the layout. A lower energy value signifies lower energy within the network, indicating a better layout quality. Since layout energy depends on the forces applied to nodes, and different algorithms may use distinct energy models [17]. So, to ensure comparability, this \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Node Count** & **Avg Edge Count** & **Steps** \\ \hline Newcomb & 17 & 40 & 15 \\ McFarland & 20 & 28 & 82 \\ email-Eu & 414 & 592 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dynamic network datasets. paper adopts the average displacement metric to assess layout stability.