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Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines 2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing Visual Analysis of Temporal Trends in Social Networks Using Edge Color Coding and Metric Timelines Udayan Khurana∗, Viet-An Nguyen∗, Hsueh-Chien Cheng∗†, Jae-wook Ahn∗†, Xi (Stephen) Chen∗, Ben Shneiderman∗† ∗ Department of Computer Science, University of Maryland, College Park † Human-Computer Interaction Lab, University of Maryland, College Park Email: {udayan,vietan,cheng,jahn,xichen,ben}@cs.umd.edu Abstract—We present NetEvViz, a visualization tool for analysis We present NetEvViz, a prototype system to visualize the and exploration of a dynamic social network. There are plenty changes in dynamic networks. The prototype is an extension of visual social network analysis tools but few provide features of NodeXL. Two temporal attributes, Start Time and End for visualization of dynamically changing networks featuring the addition or deletion of nodes or edges. Our tool extends the Time, represent the time each node or edge entered or left codebase of the NodeXL template for Microsoft Excel, a popular the network, respectively. An example of applying temporal network visualization tool. The key features of this work are (1) attributes can be found in a social network, where each vertex The ability of the user to specify and edit temporal annotations to corresponds to a specific user and each edge corresponds the network components in an Excel sheet, (2) See the dynamics to the relation between a pair of users. The start and end of the network with multiple graph metrics plotted over the time span of the graph, called the Timeline, and (3) Temporal times of a vertex are the time points when the user joins or exploration of the network layout using an edge coloring scheme leaves the social network by creating or deleting the profile, and a dynamic Timeslider. The objectives of the new features respectively. The start and end times of an edge can be used to presented in this paper are to let the data analysts, computer represent the time when a be-friend-with relation is established scientists and others to observe the dynamics or evolution in a or terminated. network interactively. We presented NetEvViz to five users of NodeXL and received positive responses. Our prototype aids the visualization of the change in a temporal network by highlighting the state of the graph I. INTRODUCTION around two timestamps. Given the two timestamps, which are specified by the user, the vertices and edges are categorized The visualization of network structures has gained much at- into groups with respect to their temporal attributes. Each tention recently. The analysis of large scale complex networks group is assigned a different color, which enables the user formed by social media has become an interesting topic with to recognize subtle patterns otherwise hard to observe in a great potential in terms of research and business. Through a homogenous graph. In order to select the points of interest, combination of computers and the cognitive capability of the the user is presented with the summary plots of certain metrics human visual system, visualization has been considered to be over the timespan of the graph. one of the most powerful approaches for network analysis. Due to the limitation of space, we have ommitted certain Dynamic networks are extensions of conventional networks details from this manuscript. At all such places, we have with temporal attributes attached to the vertices and edges. referred to a longer version [16] of this document. Since the structure of a dynamic network evolves over time, II. RELATED WORK interesting questions related to temporal attributes arise. How- ever, the analysis of a dynamic network can be challenging A. Dynamic Network Visualization without proper handling of the temporal attribute. When a Visualization is an important and useful approach to analyze static network is simply shown at a certain time, the relations network data. Network visualization techniques have been between the previous and the next timestamp disappear. Even proposed in various domains including social networks [22], if the network are shown side-by-side for comparison, the [12], biological networks [21], and computer networks [7]. differences can still be difficult to be found. The problem of representing temporal knowledge and temporal Of the many tools developed to perform network visualiza- reasoning has long been studied [5], in order to discover tion, NodeXL is a highly recognized tool based on Microsoft relations and patterns [6] and to learn from the past to Excel which supports flexible manipulation of the visual- predict, plan, and build the future [4]. The temporal dimension ization. However, there exists only limited ways to analyze is crucial for social network analysis and visualization too. dynamic network in the latest version of NodeXL. In order to With the recent popularity and availability of temporal or let users of NodeXL visually analyze the dynamic nature of longitudinal network data, there has been an increasing interest networks, more advanced features are required. in developing visualization techniques for dynamic networks. 978-0-7695-4578-3/11 $26.00 © 2011 IEEE 549 DOI Ahn et al. [3] surveyed the tasks of temporal network analysis the whole network such as number of nodes, number of edges, using temporal network visualizations. In general, these visu- density, etc. alization techniques use two popular approaches to represent Besides measurements for static networks at different points the network data. in time, a number of metrics have been proposed to explicitly The first approach uses the traditional node-link representa- quantify the change of network over time. [17] introduced tion to visualize the network. To add the temporal information, a novel dynamic centrality metric which measures how well the authors of [9] proposed a 3D layered visualization design connected a node is over time. [21] proposed two metrics to in which each newly introduced part of the network is shown quantify how groups of nodes in the network evolve over time. in a layer of its own and the composition of consecutive C. NodeXL layers represents the corresponding state of the network. [18], [8] studied two types of visualizations of temporal social NodeXL is a free and open source tool for Network Analysis networks: (1) static flip books (a combination of fixed node and Visualization [12], [1]. The tool provides a rich set of layout and dynamic social relationships), where the node functionality for visual analysis of static networks. In the sec- positions remain constant but the edges accumulate over time tion on design and implementation, we describe in more detail [10], and (2) dynamic movies where nodes move as a function the features of NodeXL and why it is a suitable candidate for of changes in relations [11]. The authors of [2] proposed extension towards dynamic network visualization. five principles for implementation of temporal visualizations III. KEY DESIGNS AND IMPLEMENTATIONS for social networks and presented two network prototypes NodeXL and TempoVis. A. Introduction to NodeXL The second approach represents a network by an adjacent As mentioned before, NodeXL provides many capabilities matrix. TimeMatrix [23] proposed a matrix-based visualization for Social Network Analysis and Visualization within Ex- for temporal network data in which each cell in the matrix cel. Visualizations can be done using different algorithms contains a bar chart glyph to show how a certain attribute of like Fruchterman-Reingold, Harel-Koren etc. Using select and the corresponding nodes or edges change over time. drag, the user can choose to manually change the layout Finally, a hybrid approach which combines both node- that was computed algorithmically. Also, the network can link-based and matrix-based representations have also been be annotated with the names or images of the nodes or used to visualize static networks such as MatrixExplorer [13], edges and the edges can be filtered based upon a numerical MatLink [14] and NodeTrix [15]. However, to the best of our attribute associated with that edge. Different nodes can be knowledge there is no hybrid approach based tool built for colored according to a cluster specification or individually temporal network visualization. as well. The tool also contains algorithms for calculating network metrics like degree centrality, betweenness centrality, B. Topological Network Statistics clustering coefficient, eigenvector centrality etc. Besides showing the node-link-based layout of the network, The ease of data storage and manipulation with Microsoft our visualization attempts to incorporate a set of network Excel, combined with the rich network visualization feature topology metrics [22], [20] to show quantitatively how the set of NodeXL make the choice of extending NodeXL instead network changes over time. This can be done by plotting a set of writing software from scratch a natural one. As explained of network topological measurements at different time points. later, we were also able to utilize Excel’s API for plotting the Network topology has been extensively studied on a wide Timeline. Finally, since NodeXL is open-source software, our range of networks including social, biological and computer future goals of distributing these changes to a large audience networks. A variety of topological metrics have been proposed of interest are better pursued through this choice. to measure important different types of network properties. In order to understand the functionalities provided by Broadly, the network topology measurements can be divided NetEvViz, we require the reader to have a working knowledge into three groups from a lower to a higher abstraction level: of NodeXL. For the readers unfamiliar with it, we provide local, group (or community) and global metrics. Note that a very brief account of NodeXL in the following sentences this categorization of metrics is not mutually exclusive. Many and strongly recommend going through the 40-page tutorial metrics at lower levels can be aggregated (e.g., sum, average that can be found at [1] and optionally refer to [12] as well.
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