Exploring temporal ego networks using small multiples and tree-ring layouts

Many of the current dynamic network visualisations methods or techniques rely on node-link force-based models that were originally developed for visualising static network snapshots. In this study, we diverge from this traditional layout approach and develop a layout for ego networks that places the time dimension in the foreground, by turning time into an element of shape. In addition to this we develop an interactive system that enables the visualisation of multiple networks simultaneously by employing small multiples. Using the proposed layout and analytical system as a grounding visual structure, we visually characterise dynamic network events in 3 different networks; the evolution of the biotechnology field, a phone call data set and a network of passenger connections of an airline. From this analysis we propose a range of ego network visual motifs that can be used as templates to identify and characterise events that are occurring in a dynamic network. Keywords-Dynamic networks, Ego networks, Small Multiples, Graph Drawing Social networks are dynamic systems in a state of constant change. People are born, form friendships, find partners, lose friends and die. Companies are created, merge, spilt and close. Traditionally, mainly because of the difficulties in collecting data, networks were studied by analysing a single snapshot of the network taken at a particular time. Today, dynamic network data is becoming increasingly available from sources such as social networking sites, telecommunication networks and customer transaction databases. Leading researchers in the field of network science have identified the study of dynamic networks as one of the next primary challenges in network theory [4]. From the very early days of social network analysis, visualisation has been a key element to the development of the field [21]. With access to dynamic network data, researchers in information visualisation are working to enhance the social scientist toolbox by developing tools and visualisation techniques that facilitate the study, understanding and exploration of network dynamics and the processes they represent. Where dynamic social network visualisation has been studied the focus has been predominantly on new nodes entering the network, large scale networks, and the use of animation techniques to convey the dynamics within the network [5], [8], [15], [26]. Recent studies [19] comparing static and animated dynamic network tasks, have shown that contrary to common expectation, static displays are generally more effective in terms of both time and accuracy, when performing analytic tasks. By contrast, instead of visualising dynamic networks using animation techniques we develop a system based on a small multiples visual interface. The principle behind small multiples design is to place several small visualisations near each other to facilitate comparison and pattern identification while maximising the ink to information ratio [35]. By displaying multiple networks simultaneously on the same page, we seek to overcome the weaknesses of animation while preserving the ability to understand the temporal nature of the network dynamics. The motivation and tasks for this study originated from studying interactions between biotechnology companies, over a span of 10 years. The initial visualisation approach relied on using multiple node-link diagrams each representing a single year displayed on the same page as small multiples. This visualisation approach soon became impractical due to the large size of the data and scale free nature of connections. The resulting diagrams were complex, difficult to understand and poor according to most graph drawing aesthetic measures [13]. Additionally, the visualisation was cluttered because it was confined to the small space dictated by the small multiples design. Our next attempt refocussed on the analyst’s tasks and asked the question, “how can the patterns of connections and events be mapped into simple visual images that can be generalised as visual motifs for dynamic network data”. We ground our study of dynamic social network visualisation in the tasks and problems faced by social scientists. 79 ACHI 2011 : The Fourth International Conference on Advances in Computer-Human Interactions Copyright (c) IARIA, 2011. ISBN: 978-1-61208-117-5 This grounding has allowed us to focus on the concept of an ego network. An ego network is a sub-network that focuses on an individual actor who is the subject of the network. The focal point of the network is called the “ego”, whereas the other actors he interacts with are called “alters”. In an ego network, only actors that are directly connected to the ego form part of the network. We describe an event as a change in the graph structure that occurs at a particular point in time. A network motif acts as a visual fingerprint of a connection pattern. While motifs for static networks are commonly employed, especially in biology, the equivalent for dynamic networks do not exist. In [36], Welser et al address ego motifs to characterise roles in social groups. In order to visualise the temporal element of activity, they use a separate visualisation called an authorline, to supplement ego motif networks with temporal data. Here, we address the need to create a simple intuitive layout that can be used to describe temporal information in ego network motifs, a visualisation that is able to tell a story in time with a single diagram. Our method to achieve this is through a tree-ring layout algorithm which converts several temporal aspects of data into visual shape elements. This form of visualisation provides a network analyst both a visual language for describing network motifs and the ability to focus on important changes in the links between actors in the network. New nodes and hence new edges are an important facet of dynamic network structure but so too are the nature, rhythm and pattern of interactions these new entities bring. Figure 1 shows the design of our small multiples system that combines a number of different network diagrams including traditional node link diagrams, matrix views and tree-ring ego networks, in one interface. In this paper, we make two main contributions. The first contribution is the development of a new visualisation based upon a tree-ring metaphor that fits well within a small multiple design and is more structured and intuitive than traditional force based node link diagrams for displaying dynamic networks. The second contribution is the design of a small multiples based network exploration system that displays multiple network visualisations at the same time. Traditionally small multiple visualisations focus on displaying attribute based visualisations such as scatter plots or bar charts. Here the small multiples system is used in favour of animation to visualise dynamic networks. The small multiples are intended to assist with the exploration of different relationships between actors in a network and help identify different roles and types of actors based on their individual connection patterns. With the help of this visualisation technique and interactive system we study 3 different data sets and provide analytical insights into the networks and the data within the networks. We also show examples of how the ego network motifs can be used to identify divergent or atypical patterns in the data. I. DYNAMIC NETWORK TASKS Prior to proposing any new visualisation design for dynamic networks, one has to clearly understand the questions and tasks that the visualisations are intended to assist with. Past research in information visualisation has grouped user tasks into taxonomies according to the type of data in question. Robert Amar et al [2] and Plaisant et al [25] have extended the general study by Shneiderman [31] to attribute data and graphs respectively. As yet, there is no published taxonomy tailored towards dynamic network tasks, so in this section we attempt to describe the most common dynamic network concepts and tasks drawn from previous studies of such data. Network structures can change in a variety of ways. However, the dynamic processes behind network evolution can be categorised in three overarching categories; formation, rewiring and dissolution [30]. These three general principles apply to both the atomic elements of the network (actors, links) and compound structures within the network, such as groups or communities [29]. If we consider the whole network structure, the formation of the network describes how the network was formed to exhibit the structure that it currently holds. If a time component is added to any structural feature in a static network, the temporal formation of that feature can be explored. This is the most basic means of enhancing a network with temporal information. A more significant aspect of change occurs when there is rewiring within a structure manifested as a change in the links between actors. The term rewiring groups all changes that alter the connections within the current structure, possibly changing the type of the structure. For example, rewiring can include a split or a merge between members of a community. Rewiring is significant in the context of network evolution because these rewiring changes can raise important questions and lead to insights into the processes that are underlying the change. Dissolution describes the process of any structural component or network member disappearing from the network structure. An example of dissolution is a scenario in which an actor in the network does not continue to from a part of the network after a certain time period. The propagation of a dissolution effect on the network is sometimes referred to