Simplifying social networks via triangle-based cohesive subgraphs

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs.Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs.Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks.However,prior works can hardly handle and visualize triangles in cohesive subgraphs.In this paper,we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed algorithm.We design and implement TriGraph,a web-based visual interface that provides detailed information for exploring and analyzing social networks.Quantitative comparisons with existing methods,two case studies on real-world datasets,and feedback from domain experts demonstrate the effectiveness of TriGraph.

Visual storytelling of Song Ci and the poets in the social–cultural context of Song dynasty

Song Ci is treasured in traditional Chinese culture,which indicates social and cultural evolution in ancient times.Despite the efforts by historians and litterateurs in investigating the characteristics of Song Ci,it is still unclear how to effectively distribute and promote Song Ci in the public sphere.The complexity and abstraction of Song Ci hamper the general public from closely reading,analyzing,and appreciating these excellent works.By means of a set of visual analysis methods,e.g.the spatiotemporal visualization,we exploit visual storytelling to explicitly present the latent and abstractive features of Song Ci.We apply straightway visual charts and lighten the burden of understanding the stories,in order to achieve an effective public distribution.The effectiveness and aesthetics of our work are demonstrated by a user study of three participants with different backgrounds.The result reveals that our story is effective in the distribution,understanding,and promotion of Song Ci。

iNet:visual analysis of irregular transition in multivariate dynamic networks

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time.They are common in multimedia applications.Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed.In this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks.We conclude features of rare categories and two types of anomalies of rare categories.Then we present a novel rare category detection method,called DIRAD,to detect rare category candidates with anomalies.We develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes.Evaluations,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.