Abstract — 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. Index Terms—Social networks, interactive graph visualization, attribute ranking, coordinated views, exploratory data analysis. 1

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.

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. 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.

This paper gives a state-of-the-art overview of available software for the statistical analysis of social networks as of Summer 2004. It reviews and compares software packages for social network analysis with respect to their statistical procedures, illustrating some procedures with example data. The choice of routines that were inspected is restricted to procedures for statistical modeling based on probability distributions (e.g., exponential random graph models, QAP correlation, statistical analysis of longitudinal network data). This definition of analysis routines excludes the extensive review of procedure-based routines based on more complex (iterative) algorithms like cluster analysis or eigendecompositions. The paper concludes with some recommendations. Key words: exponential random graph model, longitudinal network data, statistical modelling, software packages, permutation tests. 1

We apply treemap technology in the analysis of large, social network datasets—principally for examining network subgroups. A treemap is graphically-based information and exploration tool which is used in diverse fields such as computer science, finance, and human-gene research; from our experience, we find treemaps also useful in the social network analysis setting. A treemap represents hierarchical and categorical data in a mosaic form containing embedded, rectangular shapes, where the size of each shape is germane. Treemap displays are especially helpful when examining data in an interactive mode (as opposed to a static or printed form). We have found that treemaps are a powerful tool for exploring large social-networks, particularly during the exploratory data analysis phase. Their use quickly leads to a thorough perspective of the holistic characteristics of the network and to easier identification of significant subgroups; both of these perspectives may otherwise remain hidden using traditional visualization techniques. In this report, we introduce treemap technology, first broadly, then, specifically how it can be applied to social network analysis. We also show how we have actually applied treemaps to an interactive study of a large, real-world dataset. As a result of our experiences, we

Abstract. Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper, we use combination of Formal Concept Analysis and well-known matrix factorization methods to address computational complexity of social networks analysis and clarity of their visualization. The goal is to reduce the dimension of social network data and to measure the amount of information, which has been lost during the reduction. Presented example containing real data proves the feasibility of our approach. 1

Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in daily scientific practice, visualization is usually not considered to be one of them. In this article we first argue that this is due to the common practice of visualizing data rather than the information contained in it; second, we address a number of principles for effective visualization; and third, we assess visualizations generated by the software tool visone in order to explain network outcomes using these principles.