Abstract
Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.
Highlights
Social Networks are platforms for people to meet others from different backgrounds from every corner of the world
This survey is provided to the users after every visualization and the form consists of two sections: the first section is composed of eight questions on visualization techniques and the second section is composed of seven questions on the user profile
If the visualization layout used in the subgraph is of hierarchical edge bundling, we provide a scroller to adjust the tension in the bundled spline curves, which is similar to what we have in the main graph
Summary
Social Networks are platforms for people to meet others from different backgrounds from every corner of the world. Fast growing social networking sites are mostly user-friendly and it is another reason for the increase in network traffic Most of these social networks are free to use, as they profit through advertisements, number of visits per page and by providing paid features such as games, applications, tutorials, discussion forums and so on. Pairwise feature extraction is performed to get shared properties between two nodes During this process, to get the information about hierarchy as well as overlapping information of circles, the nodes that have common circles has given an option to form a connection among them [12]. In [12], an unsupervised learning method is proposed to know how profile similarity properties tend to form circles that are strongly connected. BIC is used as a criterion to select a model from the given set of finite models [15] and the model with the lowest BIC is chosen usually
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