ADDING SOCIAL NETWORK ANALYSIS FEATURES TO YAGL Michelle Alty, Maxwell Cardillo, Laura Gretz, Daniel Stahl, Dr. Eric McGregor Bridgewater College Recently, technology has been developed that allows the rendering of 3D scenes in a web browser. With BabylonJS, a 3D scene can include 3D objects with textures, lighting, shadows, and much more (Catube, 2016). In 2016, John Moran and Dr. Eric McGregor released an open-source JavaScript graph library called YAGL (pronouned 'yæ gəl) for the Babylon.js 3D framework that allows programmers to easily create and manipulate 3D graph data structures in a Babylon scene (Moran, 2016). Social Networking Analysis Social Networking Analysis (SNA) is a strategy for sociologists to study and understand social structures (Otte, 2016). SNA is not restricted to the typical social networks applications, such as Twitter or Facebook. Biologists, for example, use SNA to study the social structures of species and government agencies use SNA to understand and study the social structures of terrorists. Social Network Properties Centrality is a fundamental measure in SNA which describes how close or central one actor is to all other actors, or how centralized the entire graph is. Actor Degree Centrality is a measure that shows how many actors are connected to a certain actor relative to the number of other actors. Group Degree Centrality is the mean of the actor degree centralities.. Actor Closeness Centrality is the average of all shortest paths from the actor to all other actors. Group Closeness Centrality is the mean of the actor closeness centralities. In our implementation, the group measurements can be obtained by clicking on the Graph Statistics tab in the user interface and the actor measurements can be viewed by clicking on a specific node corresponding to an actor. Adding Capabilities for SNA The ability to utilize weighted graphs was added to YAGL. In a social network, a greater edge weight corresponds to a stronger relationship between two nodes. Visually, this means the nodes will be closer together. This was achieved by modifying the repulsion force defined by Coulomb's law for two particles, in the force-directed layout. Dividing the force by the edge weight causes the repulsion to decrease as the weight is increased. Examples of weighted versus unweighted graphs are shown below. The ability to utilize directed graphs was added to the YAGL object as a property. An example of how this would be used in a social network, is if Sally considers George a friend but George does not consider Sally a friend. This example would be visually displayed as an arrow pointing from Sally to George, but no arrow pointing from George to Sally. Adding User Interface Capabilities The YAGL scene, rendered with BabylonJS, has been incorporated into a web application built on the Webix user interface Javascript framework. This currently allows the display of information about selected edges and vertices in panes outside of the 3D scene. A right click menu provides the ability to delete selected nodes or vertices. The scene is placed inside a layout which will provide a framework for continued development of features such as analyzing, displaying and editing graph data. This interface layout is designed to allow for streamlined usability, customization and the parallel display of various types of information. Future Work Highlight a set of nodes that are connected to a selected node based on the length of the shortest path to the selected node. Identify cliques, that is, a subset of a network where path length is equal to one for every pair in the subset Analyze how the graph morphs over time. Identify structural holes and nodes that are in “bridging” positions. Analyze transitivity relationships within the graph References Cabello, Ricardo (2016) Three.js [Source code]. Available at https://github.com/mrdoob/three.js/ (Accessed April 16, 2016) Catuhe, David (2016) BabylonJS [Source code]. Available at https://github.com/BabylonJS (Accessed April 16, 2016) Moran, John; McGregor, Eric (2016) [Source code]. Available at http://github.com/erimcg/yagl (Accessed January 30, 2017) Otte, Evelien; Rousseau, Ronald (2016) “Social network analysis: a powerful strategy, also for the information sciences” Journal of Information Science Vol 28, Issue 6, pp. 441 - 453 Wasserman, Stanley and Faust, Katherine. Social Networking Analysis. Cambridge University Press,1995. Figure 1: How social networking analysis algorithms are displayed in YAGL. Figure 2: On the left is a weighted graph and on the right is an unweighted graph. Figure 3: How a directed graph is displayed in YAGL.