Adam Perer

Analyzing (social media) networks with NodeXL

We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.

Balancing Systematic and Flexible Exploration of Social Networks

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks

Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis

Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. We propose that a tight integration of these technologies in an interactive exploratory tool could dramatically speed insight development. To test the power of this integrated approach, we created a novel social network analysis tool, SocialAction, and conducted four long-term case studies with domain experts, each working on unique data sets with unique problems. The structured replicated case studies show that the integrated approach in SocialAction led to significant discoveries by a political analyst, a bibliometrician, a healthcare consultant, and a counter-terrorism researcher. Our contributions demonstrate that the tight integration of statistics and visualizations improves exploratory data analysis, and that our evaluation methodology for long-term case studies captures the research strategies of data analysts.

Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines

Evaluating visual-analytics systems is challenging because laboratory-based controlled experiments might not effectively represent analytical tasks. One such system, Social Action, integrates statistics and visualization in an interactive exploratory tool for social network analysis. This article describes results from long-term case studies with domain experts and extends established design goals for information visualization.

Systematic yet flexible discovery: guiding domain experts through exploratory data analysis

During exploratory data analysis, visualizations are often useful for making sense of complex data sets. However, as data sets increase in size and complexity, static information visualizations decrease in comprehensibility. Interactive techniques can yield valuable discoveries, but current data analysis tools typically support only opportunistic exploration that may be inefficient and incomplete.n We present a refined architecture that uses systematic yet flexible (SYF) design goals to guide domain expert users through complex exploration of data over days, weeks and months. The SYF system aims to support exploratory data analysis with some of the simplicity of an e-commerce check-out while providing added flexibility to pursue insights. The SYF system provides an overview of the analysis process, suggests unexplored states, allows users to annotate useful states, supports collaboration, and enables reuse of successful strategies. The affordances of the SYF system are demonstrated by integrating it into a social network analysis tool employed by social scientists and intelligence analysts. The SYF system is a tool-independent component and can be incorporated into other data analysis tools.

Contrasting portraits of email practices: visual approaches to reflection and analysis

Over time, many people accumulate extensive email repositories that contain detailed information about their personal communication patterns and relationships. We present three visualizations that capture hierarchical, correlational, and temporal patterns present in users email repositories. These patterns are difficult to discover using traditional interfaces and are valuable for navigation and reflection on social relationships and communication history. We interviewed users with diverse email habits and found that they were able to interpret these images and could find interesting features that were not evident to them through their standard email interfaces. The images also capture a wide range of variation in email practices. These results suggest that information visualizations of personal communications have value for end-users and analysts alike.

BELIV'08: Beyond time and errors: novel evaluation methods for information visualization

Information visualization systems allow users to produce insights, innovations and discoveries. Evaluating such tools is a challenging task and the goal of BELIV08 is to make a step ahead in the comprehension of such a complex activity. Current evaluation methods exhibit noticeable limitations and researchers in the area experiment some frustration with evaluation processes that are time consuming and too often leading to unsatisfactory results. The most used evaluation metrics such as task time completion and number of errors appear insufficient to quantify the quality of an information visualization system; thus the name of the workshop: beyond time and errors.

Making sense of social networks

Social network analysis has emerged as a powerful method for understanding the importance of relationships among interacting units in a variety of domains. However, interactive exploration of social networks is challenging because: (1) it is difficult to comprehend the characteristics and structure of networks when there are many edges and nodes, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. The contributions of our work are principles and an interface to support systematic analysis of social networks. We believe our approach will enable users to better understand the structure of networks and the social groups within.

Summaries: a special issue on evatuation for information visualization

A Comparison of Benchmark Task and Insight Evaluation Methods for Information Visualization. Chris North, Purvi Saraiya, Karen Duca This study compares two different empirical research methods for evaluating information visualizations: the traditional benchmark-task method and the insight method. The results validate the insight method’s ability to confirm results of the task method, but also show advantages of the insight method to illuminate additional types of tasks. Task efficiency and insight efficiency frequently correlate, but in some cases insight refutes task results.

Using SocialAction to uncover structure in social networks over time

I describe how SocialAction was used to find insights in an evolving social structure VAST Challenge 2008psilas Mini-Challenge 3. This analysis and SocialAction were given the award, ldquoCell Phone Mini Challenge Award: Time Visualizations of Cell Phone Activityrdquo.