Dynamic exploration of recording sessions between jazz musicians over time Darya Filippova Michael Fitzgerald Carl Kingsford Computer Science Department Learning Resources Division Computer Science Department University of Maryland University of the District of Columbia University of Maryland College Park, MD 20742 Washington, DC 20008 College Park, MD 20742 dfi[email protected] mfi[email protected] [email protected] Fernando Benadon Department of Performing Arts American University Washington, DC 20016 [email protected] Abstract—We present a new system for exploring, in an comprehend the dynamics of changing collaborations. While intuitive and interactive way, a large compendium of data about changes in band membership are easily traceable, it is hard collaborations between jazz musicians. The system consists of to assess the overall contribution of a band member over time an easy-to-use web application that marries a ego-network view of collaborations with an interactive timeline. We develop a new unless the historians are intimately familiar with the band’s measure of collaborative influence that is used to highlight strong history. A system for exploring jazz collaborations that makes and weak collaborations in the network view. The ego-network is large discography data more approachable is needed. arranged using a novel algorithm for ordering nodes that avoids Social networks such as those between collaborators or occlusion even when the network is frequently changing. Finally, friends are an object of intense study. Often, such connections the system is applied to a large, unique, hand-curated dataset of recorded jazz collaborations. The system can be accessed at are assumed to be immutable and the networks are considered http://mapofjazz.com/socialcom. static. However, there are many social interactions that violate this assumption: work colleagues, neighbors, acquaintances, I. INTRODUCTION friends, and family may all change over the course of a The evolving community of jazz musicians is an example of person’s lifetime. This is particularly true of jazz collabora- a social network where personal connections are essential [1]. tions where band members frequently come together for only In jazz, a highly collaborative art form, one person’s individual a single recording session, and where some musicians have style is a result of constant experimentation and exchange played with over a thousand other artists over their decades of techniques and ideas with fellow musicians [2]. Every long careers. Understanding changes in connectivity on a scale musician is part of a dense network of collaborators with of the whole network can provide an insight about the global many transient connections: a composer may arrange music change within the network, but has proven to be a difficult task for multiple bands simultaneously, band leaders may recruit for both algorithmic [5], [6], [7], [8] and visualization [9], [10], new members to their bands and lose them to competition, [11] approaches. The dynamism of jazz collaborations requires musicians’ skills may improve to the point where they are new approaches to visualize frequently changing networks. featured as soloists and have a prominent place in the band. Apart from the topological changes, there may be other, Study of recorded jazz collaborations can help identify influ- more subtle variations in the characteristics of relationships ences on style, explain career success, and lead to a richer over time. Node or edge attributes may change, e.g. the understanding of the progression of the jazz art form. relationship between A and B may gradually change from an The traditional means by which these collaborations are acquaintance to close collaborators, or the “importance” of A’s explored is via the compilation and study of discographies immediate collaborations may grow, thus indirectly increasing presented as lists and tables in either hard-copy books or the importance of A itself. Collectively, these changes may in computerized databases [3], [4]. These discographies list affect a life of an individual in a significant way, but these recording sessions, the roles each musician played in them, observations are lost in the sea of data when analysing the often songs and albums that were produced as a result, along network as a whole. This, along with a traditional focus on with other information. They provide an extremely rich source the lives of individual musicians, leads to the desire to have a of information from which to trace the collaborations of visualization that can be focused on subregions of the entire musicians. However, such a static and textual presentation is space of collaborations. It also leads to the need for techniques difficult to comb through and does not easily allow the user to to quantify the strength of the relationships encoded in an evolving network and to show this information effectively B. Circular and ego-network layouts through a visualization. As early as 1990 circular layouts were used for organizing In this paper, we propose a way to quantify the pairwise trees by placing the root in the center and assigning the child influence between two actors in the network based on the nodes to concentric circles with increasing radii [18] with frequency and timing of the events in which they have both nodes at the same level of a tree assigned to the same circle. participated. We provide a visualization system that displays Six et al. [19] extended the technique to work for more general much of the data available in large discographies. We visualize graphs by connecting multiple circular structures. Yee and collaborative influences with an interactive egocentric network colleagues [12] have developed the technique further to sup- view that allows users to focus on an individual and observe port nodes of different sizes (where node size is proportional large and small scale changes in collaborations. We couple to some node attribute). They adapted their layout to handle this network view with an interactive timeline that allows the dynamic graphs by showing animations of nodes traveling on user to see how influences have changed over time. We also a smooth trajectory from old locations to the new ones. introduce a novel algorithm that arranges collaborator nodes Wang, Shi, and Wen [20] experiment with a dynamic around the central musician in a way that minimizes node ego-network design where the central node and all of its occlusion and variation in node positions as the network view connections are shown at the same time. The main node has changes over time. This helps the users to maintain their several copies with each one representing the node at specific mental map of evolving collaborations. We demonstrate the point in time and linked only to those nodes with which it was utility of this approach on an extensive hand-curated collection associated during that period. This approach is not feasible if of jazz collaborations spanning almost a hundred years. the central node has many collaborators over his or her career. Gansner and Koren [21] develop heuristics that order nodes II. RELATED WORK on circle’s periphery in a way to minimize the edge crossovers A. Dynamic network visualization and to reroute some of the links to go outside the circle’s circumference. These authors suggest edge bundling for the There are two main approaches to visualizing time-varying links inside the circle to reduce clutter further. Their work networks: to show animations by constantly recomputing lay- does not consider dynamically changing links. outs at every time step [12] and to compare static snapshots of a network at several distinct time points. For either approach, C. Artist collaboration networks the objective is to highlight the differences between the network views at different time steps. Artist collaboration networks have received their fair share Brandes and Corman [13] stack network snapshots on top of attention in the wake of area of social network analysis. of each other in 3D where the nodes with the same labels are The data on interactions among artists is available in some connected by vertical columns. The graph is laid out using databases [22], has been collected through surveys [23] or a spring-embedded algorithm, and each slice shows only the manually by processing the tapes of interviews with the nodes and edges present at that time point. Diehl and Gorg¨ [14] artists [24]. Due to difficulties in data collection, these sources place the two network snapshots side by side and propose cover only a few artists and lack temporal information about three algorithms that minimize dissimilarities between the two their collaborations. For example, Gleiser and colleagues [22] representations. A recent study by Khurana et al. [15] presents base their analysis on 198 bands that were active in 1912- an extension to NodeXL [16] that aggregates the snapshots 1940. Heckathorn and Jeffri’s survey reached out to 110 of a network at two different time points into a single view. musicians in New Orleans, 264 in New York, and 300 in San The edges are colored based on the time interval at which Franscisco [23], a small fraction of the estimated 33,000 jazz they existed. Additionally, NodeXL plots the values of several musicians living in the New York. common graph properties such as node and edge counts as they Examples of applications supporting exploration of such change over time between the two time points. An approach by networks are few. An online Classical Music Navigator [25] Yi et al. [11] combines a small multiples display (a histogram helps users expand their musical interests by suggesting com- showing node degree over time) and a matrix representation posers who influenced or were influenced by a composer the of a network into a single view. users initially searched for. The Navigator offers a simple text Graph layout for the animations may be fixed or constantly and link interface.