Stress-Minimizing Orthogonal Layout of Data Flow Diagrams with Ports

We present a fundamentally different approach to orthogonal layout of data flow diagrams with ports. This is based on extending constrained stress majorization to cater for ports and flow layout. Because we are minimizing stress we are able to better display global structure, as measured by several criteria such as stress, edge-length variance, and aspect ratio. Compared to the layered approach, our layouts tend to exhibit symmetries, and eliminate inter-layer whitespace, making the diagrams more compact

Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans' perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysis approach to identify 1) groups of DAGs that are perceived as similar by the participants and 2) the reasons behind their choice of groups. Our results suggest that similarity is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the graph.Comment: Graph Drawing 2017 - arXiv Version; Keywords: Graphs, Perception, Similarity, Comparison, Visualizatio

Exploring the relative importance of crossing number and crossing angle

Recent research has indicated that human graph reading performance can be affected by the size of crossing angle. Crossing angle is closely related to another aesthetic criterion: number of edge crossings. Although crossing number has been previously identified as the most important aesthetic, its relative impact on performance of human graph reading is unknown, compared to crossing angle. In this paper, we present an exploratory user study investigating the relative importance between crossing number and crossing angle. This study also aims to further examine the effects of crossing number and crossing angle not only on task performance measured as response time and accuracy, but also on cognitive load and visualization efficiency. The experimental results reinforce the previous findings of the effects of the two aesthetics on graph comprehension. The study demonstrates that on average these two closely related aesthetics together explain 33% of variance in the four usability measures: time, accuracy, mental effort and visualization efficiency, with about 38% of the explained variance being attributed to the crossing angle. Copyright © 2010 ACM

Proximity, Communities, and Attributes in Social Network Visualisation

The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout

Communities Found by Users -not Algorithms: Comparing Human and Algorithmically Generated Communities

ABSTRACT Many algorithms have been created to automatically detect community structures in social networks. These algorithms have been studied from the perspective of optimisation extensively. However, which community finding algorithm most closely matches the human notion of communities? In this paper, we conduct a user study to address this question. In our experiment, users collected their own Facebook network and manually annotated it, indicating their social communities. Given this annotation, we run state-of-the-art community finding algorithms on the network and use Normalised Mutual Information (NMI) to compare annotated communities with automatically detected ones. Our results show that the Infomap algorithm has the greatest similarity to user defined communities, with Girvan-Newman and Louvain algorithms also performing well