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Evaluation of Graph Sampling: A Visualization Perspective

Yanhong Wu, Nan Cao, Daniel Archambault, Qiaomu Shen, Huamin Qu, Weiwei Cui
Journal PapersTVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2016) (to appear)


Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have been proposed to sample graphs and the performances of these algorithms have been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing is the impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies that investigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used in the graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results show that depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view to metric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies.

NameClarifier: A Visual Analytics System for Author Name Disambiguation

Qiaomu Shen, Tongshuang Wu, Haiyan Yang, Yanhong Wu, Huamin Qu, Weiwei Cui
Journal PapersTVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2016) (to appear)


In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.

Egocentric Analysis of Dynamic Networks with EgoLines Honorable Mention

Jian Zhao, Michael Glueck, Fanny Chevalier, Yanhong Wu, Azam Khan
Conference PapersCHI'16: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2016


The egocentric analysis of dynamic networks focuses on discovering the temporal patterns of a subnetwork around a specific central actor (i.e., an ego-network). These types of analyses are useful in many application domains, such as social science and business intelligence, providing insights about how the central actor interacts with the outside world. We present EgoLines, an interactive visualization to support the egocentric analysis of dynamic networks. Using a “subway map” metaphor, a user can trace an individual actor over the evolution of the ego-network. The design of EgoLines is grounded in a set of key analytical questions pertinent to egocentric analysis, derived from our interviews with three domain experts and general network analysis tasks. We demonstrate the effectiveness of EgoLines in egocentric analysis tasks through a controlled experiment and a case study with a domain expert.

egoSlider: Visual Analysis of Egocentric Network Evolution

Yanhong Wu, Naveen Pitipornvivat, Jian Zhao, Sixiao Yang, Guowei Huang, Huamin Qu
Journal PapersTVCG: IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2015)


Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals’ ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

Interactive Visual Summary of Major Communities in a Large Network

Yanhong Wu, Wenbin Wu, Sixiao Yang, Youliang Yan, Huamin Qu
Conference PapersPacificVis'15: IEEE Pacific Visualization Symposium 2015


In this paper, we introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a network using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.