## 2 Related Work
### Dynamic Network Visualization Approaches  
Dynamic network visualization have been a hot research topic for years that tackles the challenge of representing the evolution of relationships between entities in comprehensible, scalable, and effective diagrams [12].  
First, based on the representation of the temporal dimension, the visualization techniques can be categorized as animation-based, timeline-based, and hybrid. **Animation-based techniques**[27; 28; 29] use animations to depict changes between adjacent network layout sequences, facilitating users in observing and understanding the trends in network changes. However, due to the challenge of users remembering previous network states, animation-based methods may not support effective comparisons between network structures at different time steps. **Timeline-based techniques**[6; 30] replace animation with static views, allowing users to analyze network structure details. Nevertheless, when dealing with large scale network sequences, fully representing them within limited screen space can be challenging [12]. **Hybrid techniques** combine the animation and timeline methods for time representation, leveraging each one's advantages, such as the Flip-Book [31] presented the long network sequence with a moving time line; MultiPiles [32] designed small multiple views of piling adjacency matrices that can be animated, offering the ability to scale to networks with hundreds of temporal snapshots. The comparative study on animation-based and timeline-based shows that sequences of timeline-based static methods perform better on precision tracking [16], and animations are suitable for present broad high-level changes [33]. Therefore, we adopt a hybrid method, using the animated layout to show the overview, and providing small multiple network snapshot views for further accurate analysis and comparisons.  
Second, network representation technique is another important aspect for dynamic network visualization. This topic have be extensively studied in static and dynamic network visualizations. Comprehensive reviews can be found in [15; 34; 35]. Typical network visualization forms are node-link diagrams, matrix diagrams and space-filling diagrams. The space filling techniques, such as treemap or sunburst diagrams, have advantages on screen space utilization. This technique is suitable to hierarchical networks for the space explicitly encode hierarchical relations [15]. Comparative study on node-link diagrams versus matrices diagrams shows that the matrices perform better on larger network (100 vertices), while the node-link diagrams are more familiar to users and more readable in small size network (20 vertices) [36]. Besides the node-link diagram are widely employed and can better present the topological structures [15]. For dynamic network visualization, the time can also be an encoding dimension, the Massive Sequence View [37; 38; 39] is a timeline-based technique for dynamic network visualization. The node positions are consistent over time, and no node clutter problems, so it has demonstrated to be a more scalable solution [38]. Considering our layout algorithm is built on node-link diagram. We select the node-link diagram for our visualization system design.  
Furthermore, the dynamic network exploration is generally enhanced by multiple interactively coordinated views [40], combinations of small multiples [13], or the Level Of Details (LOD) techniques [41].  
These studies are formative for our approach. Our visualization system further supports users' exploration of dynamic network by a hybrid technique, rich interactions, and multiple linked temporal and featural views.