Tingyun Mao

Graph visualization with proper layout is widely applied to understand the relationship between entities in a complex system and the topological structure information is mainly used. Real-world graphs often have the community structures property which is ignored in many existing graph layout methods. Thus, we propose a multivariate network layout method using the force-directed method with attribute constraints. This method can effectively take into account the hierarchical structure, connection strength, and quantitative comparison between communities. First, the layout of community centers is generated by a force-directed algorithm in which node count of the community is taken as constraints to enable area balance of community; Second, community force based on node attribute is added in the force-directed algorithm to maintain the community clarity. A visualization system is also developed to allow users to interactively generate community structure-aware layout results. qualitative and quantitative evaluation of the results verifies the usability and effectiveness of the proposed method by comparing it with other methods.

Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and is influenced by the outside world. However, most of the existing methods do not fully consider the multilevel analysis of dynamic ego-networks, resulting in some evolution information at different granularities being ignored. In this paper, we present an interactive visualization system called DyEgoVis which allows users to explore the evolutions of dynamic ego-networks at global, local and individual levels. At the global level, DyEgoVis reduces dynamic ego-networks and their snapshots to 2D points to reveal global patterns such as clusters and outliers. At the local level, DyEgoVis projects all snapshots of the selected dynamic ego-networks onto a 2D space to identify similar or abnormal states. At the individual level, DyEgoVis utilizes a novel layout method to visualize the selected dynamic ego-network so that users can track, compare and analyze changes in the relationships between the ego and alters. Through two case studies on real datasets, we demonstrate the usability and effectiveness of DyEgoVis.