HCIL Tech Report Motif Simplification: Improving Network Visualization Readability with Fan and Parallel Glyphs Cody Dunne and Ben Shneiderman ⇒ Fig. 1: The left bipartite sociogram shows edit histories of the Lostpedia wiki article entitled “Four-toed-statue”. The right sociogram shows the same network after replacing the common fan and 2-parallel motifs with simplified glyphs. Abstract— Network data structures have been used extensively in recent years for modeling entities and their ties, across diverse disciplines. Analyzing networks involves understanding the complex relationships between entities, as well as any attributes, statistics, or groupings associated with them. A widely used class of visualizations called sociograms excel at showing the network topology, attributes, and groupings simultaneously. However, many sociograms are not easily readable or difficult to extract meaning from because of the inherent complexity of the relationships and the number of items designers try to render in limited space. This paper introduces a technique called motif simplification that leverages the repeating motifs in networks to reduce vi- sualization complexity and increase readability. We propose replacing motifs in the network with easily understandable glyphs that (1) require less screen space, (2) are easier to understand in the context of the network, (3) can reveal otherwise hidden relationships, and (4) result in minimal loss of fidelity. We tackle two frequently occurring and high-payoff motifs: a fan motif consisting of a fan of nodes with only a single neighbor connecting them to the network, and a parallel motif of functionally equivalent nodes that span two or more other nodes together. We contribute the design of representative glyphs for these motifs, algorithms for detecting them, a publicly available reference implementation, and initial case studies and user feedback that support the motif simplification approach. Index Terms—Network motif simplification, network analysis, social network, graph. 1 I NTRODUCTION Networks have long been common data structures in Computer Sci- ence, but have only recently exploded into popular culture with pub- lishers like the New York Times now frequently including elaborate and interesting networks with their articles. Online communities like Facebook, MySpace, Twitter, Flickr, and mailing lists (to name only a handful) enjoyed enormous growth over the last few years and provide incredibly rich datasets of interpersonal relationships called social net- works, which social scientists are now fervently exploring. Networks have also found applications in such diverse disciplines as Bioinfor- matics, Urban Planning, and Archeology. Analysis of network data requires knowledge of the connectivity, clusters, and centrality of the nodes. Statistical analysis and conven- tional visualization tools like bar and pie charts are often inadequate • Cody Dunne is with University of Maryland, E-mail: cdunne@cs.umd.edu. • Ben Shneiderman is with University of Maryland, E-mail: ben@cs.umd.edu. when faced with these varied and oftentimes immense datasets. vi- sualcomplexity.com provides many beautiful alternative network vi- sualizations, but one enduring visualization in particular models re- lationships using a node-link visualization or sociogram [1], where nodes represent actors in a community and the links or edges indicate relationships between individual actors [2]. Sociograms have only re- cently been established as tools for network analysis, but have already been put to great effect. [3, 4] successfully used sociograms to detect common social roles in online discussion newsgroups such as answer person and discussion person. Sociograms have also been applied to the study of relationships between political blogs during the 2004 U.S. Presidential Election, showing the division between liberal and con- servative communities as well as their internal interactions [5]. However, there is a huge array of possible sociograms for any given social network, many of which can be misleading or incomprehensi- ble. Visualizations of relational structures like social networks are only useful to the degree they “effectively convey information to the people that use them” [6]. In fact, the spatial layout of a sociogram can have a 1