DualNet: A Coordinated View Approach to Network Visualization

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1 DualNet: A Coordinated View Approach to Network Visualization Galileo Namata, Brian Staats, Ben Shneiderman Department of Computer Science, University of Maryland College Park, Maryland 20770, U.S staatsb@mail.umd.edu, {namatag, ben}@cs.umd.edu ABSTRACT Visualizing network data, from tree structures to fully connected graphs, is a difficult problem in information visualization. A large part of the problem is that in network data, you not only have to visualize the attributes specific to each data item, but also the attributes of links specifying how those items relate to each other. Current approaches to resolving these difficulties focus on clustering, filtering and using various layout methods. Such approaches, however, do not allow users to cross reference different aspects of the data with each other. In this paper, we present a tool called DualNet which addresses this problem by allowing users to navigate multiple coordinated views of the same network. We evaluate the tool with a case study involving an communication network and find that multiple coordinated views improve navigation and provide additional insight in networks with multiple node and link types. KEYWORDS Social networks, Coordinated Views, Information Visualization, Interactive Graph Visualization, Exploratory Data Analysis 1 INTRODUCTION There is an increasing number of network data made available each day, especially on the Internet. These range from inferred networks generated by communication and collaboration graphs, such as communications and instant messages, to more explicit networks defined by social networking sites such as MySpace and Friendster. Due to the prevalence of these networks, there is growing interest in visualizing and exploring these networks for historical (i.e. exploring government communications), financial (i.e. viral marketing) and legal (i.e. looking at the s of investigated companies) reasons. Visualizing network data, however, is difficult by its nature. Unlike other forms of data, visualizing network data not only involves visualizing various attributes of each data point, it also involves visualizing the attributes of links that specify how these data points relate to each other. As a result of trying to show all these pieces of information, visualizations of network data are often difficult to explore, suffering from things like occlusion and clutter. Various approaches have been proposed to address these problems ranging from filtering and clustering of data, to various ways to lay out networks in a display [1,3,9,10,19]. These approaches, however, focus only on visualizing a network in a single display, limiting the amount of information that can be shown. Moreover, past approaches do not allow for comparison of different parts and aspects of the data. As a result such approaches do not allow users to cross reference different aspects of the data with one another. To address these problems, we created a tool called DualNet, a coordinated view approach to network visualization. In section 2, we further motivate this problem by describing it in the context of exploring an communication network. We review some of the relevant works in section 3 and describe our approach and implementation in section 4 and 5. We then provide results from our evaluation of our approach in section 6 and 7. We discuss future work in section 8 and conclude in section 9.

2 2 MOTIVATING PROBLEM Recent estimates state that the number of messages sent in a day exceeds 2.25 billion [21]. As such a major form of communication, there is great interest in archiving and analyzing large collections for both historical and legal reasons. To this end, there have been a number of tools created to work with archives. In the field of information visualization, the standard approach of representing the communications as a node link graph, with nodes as the individuals and edges representing exchange, have been successful at various tasks including highlighting anomalies in the traffic counts and illustrating word usage in communications [8,12]. Although these tools have provided great insight into these collections, they focus too much on the communications graph and not enough on a major property of the archive, the subset of the graph representing a formal underlying social network. The exploration of any archive must take into account the underlying social network to provide context for the communications. One example of this can be seen in the most widely studied archive, the Enron collection [12]. In this collection, documentation of the employees from whose accounts the collection was created from lists one of the most active individuals in the collection as Mark Taylor, a senior level executive. Looking solely at the communications graph, this identification seems normal. Mark Taylor sends a number of s, as an executive would, and a quick glimpse at the content may support this. Simultaneously looking at a different view of the network arranged in an organizational chart for comparison, however, we see that Mark Taylor seems to communicate frequently with both high level executives and entry level associates. Moreover, by looking at the network at the level of divisions within a company, we find that Mark Taylor also s two very different divisions frequently. Looking at these views for other individuals in the organization, specifically with other senior level executives, we can establish this is not common and now have reason to look at Mark Taylor further. In this case, looking at both the underlying social network and group structure, as well as the communication graph, revealed that there are two Mark Taylor, a Mark A. Taylor and a Mark E. Taylor, in the company, an executive and a lower level employee from two different divisions, whose accounts were mistakenly merged in the collection. Providing two different views of the same network allow for unexpected anomalies to become visible that would normally be undetectable. 3 RELATED WORKS 3.1 Network Visualization Network visualization is a challenging endeavor that becomes increasingly difficult as networks increase in size [3]. This is particularly true of the standard node-like representation of network data. One example of this is Vizster [1], a visualization tool written using the Prefuse [22] toolkit, for visualizing the specific domain of online social networks. The tool approaches the problem of clutter by showing only a small number of nodes and edges initially and allowing the user to expand from that. Although this does help, it does not allow the user to get an overview of the whole collection as advocated by the widely used in the Visual Information Seeking Mantra, Overview first, zoom and filter, then details-on-demand [22]. Other representations, such as the Matrix-based [2] and TreeMap [19] have been explored to complete specific tasks and display specific types of networks. Such representations, however, are still limited by in the number of dimensions and relationship types they can coherently display in a single visualization [2]. 3.2 Coordinated Views To address shortcomings in previous approaches, we look to coordinated views. Coordinated Views are a powerful technique for providing the user with feedback about the relationships between two or more visualizations and has been used extensively in navigating multidimensional data. One example of this is PairTrees [13], a tool for visualizing hierarchical graphs (trees), which utilizes Treemaps [19] and SpaceTree [18] as two separate views supporting coordinated brushing and linking between. Our approach is similar to this but we expand it to general network graphs, as well as focus only on different views of the same graph,

3 rather than links between different data sets. Next, a similar concept to viewing two structures in a network was explored by Burch and Diehl [14] where they were concerned with visualizing object trees with an underlying taxonomy structure. Although this approach has its advantages, it can be argued that it also introduces visual clutter and cognitive load by representing the different structures on top of one another in the same view rather than sideby-side in two separate views. Snap-Together [15] is a visualization architecture that allows users to coordinate views of multiple visualization tools, but requires the visualization tools to preexist and the interactions between the views are limited. Mukherjea et. al. [16] used dual network visualizations to grasp large network datasets, but the level of user integration and options where lacking and is not suitable for exploring the complexities between hierarchies underlying social networks like company power structure. As indicated by Baldonado, Woodruf et al. [17] multiple views may also add cognitive load to the user and thus, how many views and the value of each view must be considered when designing tool with coordinated views. Finally, a tool called SocialAction introduces a systematic approach to analyzing social networks with attribute ranking and coordinated views using many standard social network analysis measures like centrality and degree. The coordinated views in this tool, however, is not used between multiple representations of the same network, but only to links from different components such as the search and ranking results. 4 APPROACH Rather than using a single visualization to view a social network, the visualization is differentiated into separate views in order to better analyze the various aspects of the data. The user is able to control and manipulate each view independently by changing various network properties of graph type, size, color, filtering, and more. Linking between the views allow the user to identify any selected nodes or edges from one view to the other, enhancing exploration of the network. A useful example is aggregating one view by a particular data attribute in order to reduce clutter while leaving the other view un-aggregated. Through linking, the user is able to see all the nodes in the un-aggregated view that belong to the superset node in the other view. 5 IMPLEMENTATION The DualNet implementation is built with the Java based Prefuse information visualization toolkit [22]. Java was chosen for its portability and Prefuse for its extensive support for network visualization. Moreover, Prefuse supports other visualization techniques that may be useful for future versions where we may view aspects of the same network in other displays such as scatter plots, bar graphs and tree maps. In this version, the tool is limited to two visualization views of the data though nothing in our approach forces this restriction. The different views of the network are identical to one other in terms of user interactivity and options. This reduces user learning curve and increases ease of use while maintaining a high degree of user control and interactivity. For a view, there are four tabs (Network, Filters, Properties, Search) that provide users with information and controls for manipulating the network (Figure 1). The Network tab provides the user with the options for displaying the network. It includes various layout methods as well as the ability to change the properties of the visualization based on attributes of the data. For this version, we limit ourselves to node link representations of the data and thus controls provided are limited to type, color and sizes of nodes and edges. The option, Node Types, allow for clustering nodes by a single string data attributes. Node color allows the user to specify what string or numeric attribute to vary the color on while Node Size allow for changing nodes based on the values of numerical attributes. Similar options for edges are also under the Network tab. The available graph layouts for the current implementation were limited to four network layouts offered in the Prefuse toolset.

4 Figure 1. The same social network represented in two coordinated linked views. Left; is a Radial tree view with the filter option tab selected. Right; is a Node-Link tree view with the network option tab selected. Both views have nodes colored by title. The Filters tab gives the user the ability to filter the network based on attributes of the data. Options include, filtering node degree and data based numeric attributes of the nodes and edges. This provides for reducing the clutter of the graph and making the network easier to manage and view. We also provide a way to recenter the display as appropriate in some lay outs. For example, when using the layout RadialTreeLayout, re-centering will place the currently selected node in the center of the display. A checkbox is also present, show only highlighted, that once selected, only those items in the other graph that are selected will be visible in the current graph. The Properties tab is for displaying detailed information about a node or an edge with its corresponding end nodes currently selected in the view. Tool tips on the nodes also provide the user with information. For readability, we show nodes as labels, instead of circles, for when there are only a few nodes, less than 25, in the graph. The Search tab allows the user to search for a specific node in the data. Matching nodes are shown in the results list and are highlighted on the display upon selection. Also, we support searching for additional data on the current dataset. In this version, related to the dataset we use for evaluation described in the next section, we allow the user to search for s from a single individual or between two. A list of messages is returned allowing the user to select one of the s in the list for viewing in a separate window. We also implement controls in the network display itself. The user can navigate each view with the mouse by panning and zooming in addition to dragging individual nodes. Moreover, selection of a node or edge in one view will highlight matching items in the other view. For example, if one view groups people with the same manager as a single node and the other shows each node as an address, selection of a manager node in the first view will highlight all the nodes in the second view which correspond to addresses that have that exact manager. The same is true in the reverse, where selecting an address in the second view will highlight the node representing the manager of that address in the first view.

5 6 EVALUATION DualNet was evaluated on a subset of the well studied Enron collection [12]. The subset contains addresses from , selected specifically because we have manually annotated information about the titles and management structure for the individuals who use these addresses [4,5], as well as all the communications between all those individuals. The network graph constructed from this data set resulted on a base node set consisting of 120 addresses and 2700 edges representing the amount of messages sent between nodes. The node and edge properties are set as follows: Node Properties: 1. address Internal Enron employee address. We remove suffix for readability. 2. name Name of the person using the address. 3. title Title of that individual who using the corresponding address from numsent Number of s sent by the address in numreceived Number of s received by the address in numtotal Number of total s send and received by this address in mgr address of the direct manager using the address in Edge Properties: 1. type Edge type (to, from, total) 2. count The count of this address in issubmgr Set to yes if this edge represents communication sent by a subordinate to its manager We performed a case study of the tool with an Associate Professor at University of Maryland and a researcher at Johns Hopkins University Applied Physics Laboratory who have done extensive work in role prediction and relationship identification on the Enron dataset collected [4,5]. Each person was given a 30 minute preview of the tool and then allowed to explore the tool for as long as they wanted, but no less than 30 minutes. We were available to answer any questions about the tool during their exploration. They were then instructed to provide detailed feedback of bugs, feature requests, comments and criticisms of the tool. They were also requested to point out any interesting results from their exploration. 7 RESULTS The overall feedback for the tool was positive. Both users felt that a multiple view approach was definitely appropriate for network data, specifically in the case of social networks where there are often many different edge types. They felt that a dual interface was a cleaner and more natural approach to showing a large number of node and edge attributes. Moreover, they felt that the multiple interfaces were useful in iteratively navigating the graph to nodes and edges of interest. A reference was made to how the interface is similar to a bird s eye view of the data, common in many image processing applications. They felt it was more powerful, however, since the view was not restricted to be of the same type and can be at different levels of abstraction. We also received feedback about interesting aspects of the tool and data from our demonstration and from their experience with the tool. We present some of those results in the section 7.1. We also discuss any criticisms and features requested in section FEEDBACK NETWORK COMPARISONS One feature the users liked was the ability to focus on two different parts of the same network and compare them side by side, whether by zooming in to one area of the network or filtering the two networks differently. In Figure 2, we illustrate this by showing the ego networks, a sub-network consisting of a node and all nodes whom that node is directly connected to, of two individuals, one that is an executive committee member and another that is a vice president. Initially, we expected the two networks to be very similar since they are both upper management positions. We find however that they do vary greatly. The executive committee person has a smaller ego network. Also, they

6 have different types of people they communicate with as in the case with the executive committee individual not communicating with analysts. This provides insight to how interactions between different titles differ in the network MANAGER VS. TITLES A second feature they liked was the ability to represent the same graph multiple ways and seeing the interaction in it. One example discussed is shown in Figure 3 where the left interface clusters nodes based on their direct manager while the right interface clusters them in terms of the title. In this visualization, the user is able to see which titles report to which managers. For example, selecting scott.neal on the left (highlighted as green) on the left, we see the titles Senior Specialists, Associates, Managers, and Directors highlighted on the left in pink. This shows that in our collection, individuals from a variety of titles report directly to Scott Neal. Moreover, we note that this gives us additional information about who Scott Neal was, pointing out that this person must be in upper management. Our users noted that the alternative way this could have been uncovered would be just using coloring of the nodes. They felt, however, that that would not have been as easy to understand since the number of pairwise combinations of the different titles and managers would have made keeping track of what colors mapped to what value difficult when using a regular legend. Figure 2. Social network of an executive committee and vice president one degree away and colored by title ANOMALY DETECTION Through the use of the coordinated views, the users felt that certain anomalies in the data became more noticeable. In initial explorations of the data, we set the node colors to represent different titles and asked them about to count how many titles there were and how many of each title were available in the graph. With 11 titles however, they couldn t get a sense of how many of each title there was by just looking at the single interface. However, by using coordinated views, setting one view to represent nodes as titles and the other as addresses, without setting the color attribute, they were able to count exactly how many titles there were and by clicking on a specific title, see the corresponding addresses with that specific title stand out from all the other nodes. One case of this is shown in Figure 4 where one can quickly and easily see that there are 9 individuals with the title of senior specialist.

7 Moreover, we can see easily how these titles are outliers in the communication graph as shown by how these nodes make up 5 of the 6 nodes, isolated in the bottom of the display, who only each other. Figure 3. The left view clusters nodes by their manager and the right view has the nodes by their titles of those people. Figure 4. Using different graph layouts and node types allows the user to see that the senior specialists (left) are outliers in the communication graph (right).

8 7.1.4 HIERARCHY GENERATION The most compelling results discussed during the evaluation of this tool are with respect to what the tool shows us about the hierarchy of these communications. This is the first time title and direct report information were put together for this collection, something which both users were eager to see, and the combination shows us a glimpse of how the management structure was in Enron (Figure 5). By setting the node type to cluster by title and only showing the edges for direct reports (with an arrow starting from node A to node B meaning node B is the manager of node A), the tool shows that the highest title in our collection is executive committee. This also shows that unlike initial assumption that only directors report to vice presidents cited in one of the user s works [5], we found that the role of vice president, for Enron, was more dynamic. Four different title types report to vice presidents. Looking further, we see that for some reason, associates, the lowest title defined in Enron documentation, also report to vice presidents. By clicking on that link and looking at the corresponding highlights in the next display, we can see that there is only one case of this relationship and is worth examining further. Figure 5. The left view displays the title hierarchy by representing nodes as titles and edges as to whom each title reports to and the right view displays addresses as nodes and direct report communications as links. 7.2 CRITICISMS We also received some criticisms about the tool which we discuss here. First, although they felt that the tool allowed for general exploration, they felt that more specific data representations would make the tool more effective. For example, in case of this data, developing our own layout algorithm, rather than using general network layout algorithms, to automatically arranging the nodes in terms of power, with higher ranked individuals shown above their subordinates, would be useful more useful. Another criticism is that the tool currently only allow one node or edge to be selected at a time. Our users felt that the capability to select whole groups of nodes would allow for better analysis. On the same note, they felt that the color

9 highlighting may not be enough, especially for larger networks where nodes may be very small. Providing a link or line between the two views or using some sort of animation, linking corresponding selections, might be needed. They also stated that although this is useful for networks with multiple edge and node types, it doesn t add anything for very simple networks where we only have one node and edge type. In those cases, the limited filtering, grouping and searching capabilities we implemented were not on par with currently available network visualization tools. We plan on addressing these issues in future versions of our tool. 8 FUTURE WORKS There are a few shortcomings of DualNet, as described in the previous sections, which we plan to address in future versions. We also plan on making additional changes to make the tool more general. Currently, DualNet does not allow users to import their own data. Also, the current version of DualNet only allows for node link diagram visualizations. We would also like to explore other representations such as matrix based and TreeMaps (for the power structure) to see if integrating these views can improve usability. Also, we want to allow for more than just two views of the same networks as we feel it would give greater flexibility to users. The current implementation of DualNet provides users with many parameters for each view. A useful addition would be to provide a save feature to allow users to save the state of each view given the current setting of parameters. We would also like to do a more controlled study comparing our approach to other network visualization tools to see exactly what the strengths and weaknesses of each approach is. We also want to explore other, richer and larger network data to see if we can repeat our results on those networks. 9 CONCLUSION There are many challenges in visualizing large social networks. Here we attempted to alleviate a few of the inherent problems of visualizing relationships between nodes and the visual clutter of edges depicting those relationships by using coordinated views. DualNet provides a high degree of graph customization based on a provided dataset. The degree of customization is enhanced by side-by-side coordinated views allowing the user to represent the graph data in multiple layouts, colors, sizes, and various applied filters. We have shown that using coordinated views, we can explore various aspects of the same dataset to provide perspectives a single display may not show. REFERENCE 1. Heer, J., Boyd, D., Vizster: visualizing online social networks, IEEE Info Visualization 2005, Ghoniem, M.; Fekete, J.-D.; Castagliola, P., A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations, IEEE Info Visualization 2004, Shneiderman, B. and Aris, A., Network Visualization by Semantic Substrates, IEEE Info Visualization Diehl, C., Getoor, L. and Namata, G. Name Reference Resolution in Organizational Archives. 6th SIAM Conference on Data Mining, Bethesda, MD, April Namata, G., Getoor, L. and Diehl, C. Inferring Formal Titles in Organizational Archives. A Workshop at the 23rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, June Wattenberg, M Visual exploration of multivariate graphs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Montréal, Québec, Canada, April 22-27, 2006). R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI '06. ACM Press, New York, NY, Grippa, F., Zilli, A., Laubacher, R., Gloor, P. may not reflect the social network, Proceedings International Conference NAACSOS - Annual Conference of the North American Association for Computational Social and Organizational Sciences conference (Indiana, US) June 2006, Notre Dame, IN, USA. 8. Contrasting portraits of practices: visual approaches to reflection and analysis. AVI '06: Proceedings of the working

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