Title
SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization
Abstract
Increasingly, social network datasets contain social attribute information about actors and their relationship. Analyzing such network with social attributes requires making sense of not only its structural features, but also the relationship between social features in attributes and network structures. Existing social network analysis tools are usually weak in supporting complex analytical tasks involving both structural and social features, and often overlook users' needs for sensemaking tools that help to gather, synthesize, and organize information of these features. To address these challenges, we propose a sensemaking framework of social-network visual analytics in this paper. This framework considers both bottom-up processes, which are about constructing new understandings based on collected information, and top-down processes, which concern using prior knowledge to guide information collection, in analyzing social networks from both social and structural perspectives. The framework also emphasizes the externalization of sensemaking processes through interactive visualization. Guided by the framework, we develop a system, SocialNetSense, to support the sensemaking in visual analytics of social networks with social attributes. The example of using our system to analyze a scholar collaboration network shows that our approach can help users gain insight into social networks both structurally and socially, and enhance their process awareness in visual analytics.
Year
DOI
Venue
2012
10.1109/VAST.2012.6400558
IEEE VAST
Keywords
DocType
Citations 
social network,structural feature,information collection,network structure,social network datasets,social attribute,visual analytics,social feature,social network analysis tool,social attribute information,scholar collaboration network,interactive visualization
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaolong Zhang127821.91
Liang Gou226915.49
Airong Luo3254.33
Patricia F. Anderson400.34