# DyEgoVis: Visual Exploration of Dynamic Ego-Network Evolution
## 2 Related Work
### Dynamic Ego-Network Visualization  
Dynamic ego-network visualization has aroused great interest among researchers. Different methods adopt different visualization techniques and analyze the evolutions of dynamic ego-networks from different perspectives. A dynamic ego-network can be visualized as a radial network diagram where alters are placed around the ego and the temporal ego-alter relationship is encoded along the radius. [PERSON] [30] employed different color segments to represent time. The thickness of a color segment encodes the weight of the corresponding ego-alter edge (e.g., number of co-authored papers in the year). This work does not allow users to compare the evolutions of multiple dynamic ego-networks. To analyze and compare multiple dynamic ego-networks, [PERSON] et al. [31] combined small multiples and the radial layout. Through comparative analysis, users can explore the evolution patterns of different dynamic ego-networks. However, the readability of each radial layout is easily influenced by the size of the dynamic ego-network and the number of timesteps. The above methods only display the relationships between the ego and one-level alters. Thus, it's difficult for them to support some individual-level tasks involving two-level alters such as whether two-level alters may become one-level alters. The methods based on timeline and node-link diagrams can present ego-alter and alter-alter relationships in the specified timespan. [PERSON] et al. [3] proposed a 1.5D visualization technique that draws an aggregated node-link diagram consisting of ego-alter and alter-alter relationships along the timeline. It can provide an ego-network overview in a timespan but fails to reveal the evolution patterns of ego-alter relationships. To reduce the visual complexity of dynamic ego-networks, EgoNetCloud [32] simplifies snapshots using multiple strategies including pruning edges, compressing nodes and filtering. This makes it difficult for users to track and compare specific ego-alter relationships. Recently, [PERSON] et al. [33] used small multiples to display the node-link diagram of each ego-network snapshot. However, the readability of the node-link diagram is easily influenced by the number of alters. Moreover, it is still difficult for users to track ego-alter relationships. The aforementioned methods focus on tackling individual-level tasks. To tackle global-level tasks, some methods reduce the snapshots of dynamic ego-networks to 2D points. The egoSlider system [4] utilizes MDS [34] to generate an overview view for each timestep in which each dot represents a snapshot at the timestep. With the help of the overview views, users can complete some global-level tasks such as identifying clusters and outliers. In addition, it provides timeline-based glyph designs to support some individual-level tasks such as comparing characteristics of multiple dynamic ego-networks. However, this method requires users to examine the generated overview views one by one for some global-level tasks such as identifying a group of dynamic ego-networks with similar evolution patterns. This process is very tedious for users, especially when there are many timesteps. Moreover, it does not support local-level tasks such as identifying similar evolution states. Both TMNVis [35] and MENA [36] employ a similar approach to egoSlider to explore multivariate dynamic ego-networks. They do not fully support global-level and local-level tasks. ManyNets [37] can present the snapshots of dynamic ego-networks as rows in a tabular visualization, thereby allowing users to compare and analyze the changes in the characteristics of the dynamic ego-network by columns. It is not suitable for analyzing the changes in ego-alter relationships. Different from previous work, Segue [38] allows the user to interactively construct a spatial layout where each point represents the entire dynamic ego-network rather than a snapshot. Through the spatial layout, users can find dynamic ego-networks with similar or abnormal evolution patterns.