Who talks to whom: an evaluation of a call log visualization

In this paper, we present a case study of applying visual analytics methods to explore a dynamic social network. The visualization and analysis of this kind of data is challenging because of its relational and temporal nature. We illustrate a method and its prototypical implementation that integrate: the combination of three views based on node-link diagrams, a dynamic layout, the visualization of social network analysis metrics, and specific interaction techniques for tracking node trajectories and connections over time. We discuss how this integration of visual, interactive and analytical methods, driven by perceptual principles, can help users with gaining insights when examining dynamic organizational network data.

2004

ABSTRACT This paper introduces an approach for organizational redesign and optimization of communication flows based on temporal analysis of communication patterns in groups of people. Our Temporal Communication Flow Visualizer automatically generates interactive movies of communication flows among individuals by mining email log files and other communication archives.

2002

Key issues:• Teaching face-to-face you get a sense of the 'buzz'of the in-class discussions, degrees of student participation and how the class is doing over time. Teaching remotely you often lose these easily acquired qualitative hints.• What can we build to provide lightweight qualitative visualizations that can be used to help a person understand conversational buzz and so inform possible interventions?• The design criteria involve providing the ability to get rough impressions and spot key problems at a glance.

2010

Visualization plays a crucial role in understanding dynamic social networks at many different levels (i.e., group, subgroup, and individual). Node-link-based visualization techniques are currently widely used for these tasks and have been demonstrated to be effective, but we found that they also have limitations in representing temporal changes, particularly at the individual and subgroup levels. To overcome these limitations, we present a new network visualization technique, called "TimeMatrix," based on a matrix representation. Interaction techniques, such as overlay controls, a temporal range slider, semantic zooming, and integrated network statistical measures, support analysts in studying temporal social networks. To validate our design, we present a user study involving three social scientists analyzing inter-organizational collaboration data. The study demonstrates how TimeMatrix may help analysts gain insights about the temporal aspects of network data that can be subsequently tested with network analytic methods.

2012

In recent years, the analysis of dynamic network data has become an increasingly prominent research issue. While several visual analytics techniques with the focus on the examination of temporal evolving networks have been proposed in recent years, their effectiveness and utility for end users need to be further analyzed. When dealing with techniques for dynamic network analysis, which integrate visual, computational, and interactive components, users become easily overwhelmed by the amount of information displayed-even in case of small sized networks. Therefore we evaluated visual analytics techniques for dynamic networks during their development, performing intermediate evaluations by means of mock-up and eye-tracking studies and a final evaluation of the running interactive prototype, traceing three pathways of development in detail: The first one focused on the maintenance of the user's mental map throughout changes of network structure over time, changes caused by user interactions, and changes of analytical perspectives. The second one addresses the avoidance of visual clutter, or at least its moderation. The third pathway of development follows the implications of unexpected user behaviour and multiple problem solving processes. Aside from presenting solutions based on the outcomes of our evaluation, we discuss open and upcoming problems and set out new research questions.

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011

Visualizations of static networks in the form of node-link diagrams have evolved rapidly, though researchers are still grappling with how best to show evolution of nodes over time in these diagrams. This paper introduces NetVisia, a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time. NetVisia's novel contributions to network visualizations are to (1) cluster nodes in the heat map by similar metric values instead of by topological similarity, and (2) align nodes in the heat map by events. We compare NetVisia to existing systems and describe a formative user evaluation of a NetVisia prototype with four participants that emphasized the need for tooltips and coordinated views. Despite the presence of some usability issues, in 30-40 minutes the user evaluation participants discovered new insights about the data set which had not been discovered using other systems. We discuss implemented improvements to NetVisia, and analyze a co-occurrence network of 228 business intelligence concepts and entities. This analysis confirms the utility of a clustered heat map to discover outlier nodes and time periods.

The visualization and analysis of dynamic networks have become increasingly important in several fields, for instance sociology or economics. The dynamic and multi-relational nature of this data poses the challenge of understanding both its topological structure and how it changes over time. In this paper we propose a visual analytics approach for analyzing dynamic networks that integrates: a dynamic layout with user-controlled trade-off between stability and consistency; three temporal views based on different combinations of node-link diagrams (layer superimposition, layer juxtaposition, and two-and-a-half- dimensional view); the visualization of social network analysis metrics; and specific interaction techniques for tracking node trajectories and node connectivity over time. This integration of visual, interactive, and automatic methods supports the multi- faceted analysis of dynamically changing networks.

In recent years, we have witnessed a dramatic popularity of online social networking services, in which millions of people publicly communicate for a kind of mutual friendship relations. Social network research is one of the fastest growing academic areas as it is continuously expanding within our society. One key element of this field of research is social network visualization, which refers to the use of sociograms / illustrative diagrams of the joins that connect various actors in social networks. The use of graphical representations is one of the main defining properties of social networks. Researchers make use of pictorial images of social networks in order to communicate and understand the content and patterns within social networks. In this paper, we have made every possible effort to remove the fear from mind of people that understanding networks is a difficult process as it is difficult to visualize, navigate, and find patterns in networks. For this, we begin by defining what constitutes a social network analysis (SNA) and then present our introduction of SNA by drawing basic concepts of social networks, and then discussed about various visualization approaches used and its advantages along with most popularly used visualization tools.

2011

This paper explores how aggregated behavioral data might help people reflect on patterns in their lives using pieTime, a visualization that presents communication activity aggregated at levels from hours in a day to months in a year. pieTime builds on recent work in conversation visualization and lifelogging by focusing on rhythms rather than details and supporting reflection across different media. An evaluation with 15 people supports findings from prior work about the importance of particular details and storytelling in tools that support reflection, even when the design goals emphasize higher-level patterns. Still, aggregate patterns provide additional insight into personal behavior, suggesting that systems that integrate both particulars and patterns may be especially valuable, especially when they also help people build and manage their identities.

PLOS ONE, 2016

Node-Link diagrams make it possible to take a quick glance at how nodes (or actors) in a network are connected by edges (or ties). A conventional network diagram of a "contact tree" maps out a root and branches that represent the structure of nodes and edges, often without further specifying leaves or fruits that would have grown from small branches. By furnishing such a network structure with leaves and fruits, we reveal details about "contacts" in our ContactTrees upon which ties and relationships are constructed. Our elegant design employs a bottom-up approach that resembles a recent attempt to understand subjective well-being by means of a series of emotions. Such a bottom-up approach to social-network studies decomposes each tie into a series of interactions or contacts, which can help deepen our understanding of the complexity embedded in a network structure. Unlike previous network visualizations, ContactTrees highlight how relationships form and change based upon interactions among actors, as well as how relationships and networks vary by contact attributes. Based on a botanical tree metaphor, the design is easy to construct and the resulting tree-like visualization can display many properties at both tie and contact levels, thus recapturing a key ingredient missing from conventional techniques of network visualization. We demonstrate ContactTrees using data sets consisting of up to three waves of 3-month contact diaries over the 2004-2012 period, and discuss how this design can be applied to other types of datasets.