Tarik Crnovrsanin

Proximity-based visualization of movement trace data

The increasing availability of motion sensors and video cameras in living spaces has made possible the analysis of motion patterns and collective behavior in a number of situations. The visualization of this movement data, however, remains a challenge. Although maintaining the actual layout of the data space is often desirable, direct visualization of movement traces becomes cluttered and confusing as the spatial distribution of traces may be disparate and uneven. We present proximity-based visualization as a novel approach to the visualization of movement traces in an abstract space rather than the given spatial layout. This abstract space is obtained by considering proximity data, which is computed as the distance between entities and some number of important locations. These important locations can range from a single fixed point, to a moving point, several points, or even the proximities between the entities themselves. This creates a continuum of proximity spaces, ranging from the fixed absolute reference frame to completely relative reference frames. By combining these abstracted views with the concrete spatial views, we provide a way to mentally map the abstract spaces back to the real space. We demonstrate the effectiveness of this approach, and its applicability to visual analytics problems such as hazard prevention, migration patterns, and behavioral studies.

Visual Reasoning about Social Networks Using Centrality Sensitivity

In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.

Visual recommendations for network navigation

Understanding large, complex networks is important for many critical tasks, including decision making, process optimization, and threat detection. Existing network analysis tools often lack intuitive interfaces to support the exploration of large scale data. We present a visual recommendation system to help guide users during navigation of network data. Collaborative filtering, similarity metrics, and relative importance are used to generate recommendations of potentially significant nodes for users to explore. In addition, graph layout and node visibility are adjusted in real‐time to accommodate recommendation display and to reduce visual clutter. Case studies are presented to show how our design can improve network exploration.

Visualization techniques for categorical analysis of social networks with multiple edge sets

Abstract The growing popularity and diversity of social network applications present new opportunities as well as new challenges. The resulting social networks have high value to business intelligence, sociological studies, organizational studies, epidemical studies, etc. The ability to explore and extract information of interest from the networks is thus crucial. However, these networks are often large and composed of multi-categorical nodes and edges, making it difficult to visualize and reason with conventional methods. In this paper, we show how to combine statistical methods with visualization to address these challenges, and how to arrange layouts differently to better bring out different aspects of the networks. We applied our methods to several social networks to demonstrate their effectiveness in characterizing the networks and clarifying the structures of interest, leading to new findings.

Egocentric storylines for visual analysis of large dynamic graphs

Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.

Social Network Discovery Based on Sensitivity Analysis

This paper presents a novel methodology for social network discovery based on the sensitivity coefficients of importance metrics, namely the Markov centrality of a node, a metric based on random walks. Analogous to node importance, which ranks the important nodes in a social network, the sensitivity analysis of this metric provides a ranking of the relationships between nodes. The sensitivity parameter of the importance of a node with respect to another measures the direct or indirect impact of a node. We show that these relationships help discover hidden links between nodes and highlight meaningful links between seemingly disparate sub-networks in a social structure. We introduce the notion of implicit links, which represent an indirect relationship between nodes not connected by edges, seen as hidden connections in complex networks. We demonstrate our methodology on two social network data sets and use sensitivity-guided visualizations to highlight our findings. Our results show that this analytic tool, when coupled with visualization, is an effective mechanism for discovering social networks.

Stimulating a blink: reduction of eye fatigue with visual stimulus

Computers make incredible amounts of information available at our fingertips. As computers become integral parts of our lives, we spend more time staring at computer monitor than ever before, sometimes with negative effects. One major concern is the increasing number of people suffering from Computer Vision Syndrome (CVS). CVS is caused by extensive use of computers, and its symptoms include eye fatigue, frequent headaches, dry eyes, and blurred vision. It is possible to partially alleviate CVS if we can remind users to blink more often. We present a prototype system that uses a camera to monitor a users blink rate, and when the user has not blinked in a while, the system triggers a blink stimulus. We investigated four different types of eye-blink stimulus: screen blurring, screen flashing, border flashing, and pop-up notifications. Users also rated each stimulus type in terms of effectiveness, intrusiveness, and satisfaction. Results from our user studies show that our stimuli are effective in increasing user blink rate with screen blurring being the best.

Cell phone mini challenge award: Intuitive social network graphs visual analytics of cell phone data using mobivis and ontovis

MobiVis is a visual analytics tools to aid in the process of processing and understanding complex relational data, such as social networks. At the core of these tools is the ability to filter complex networks structurally and semantically, which helps us discover clusters and patterns in the organization of social networks. Semantic filtering is obtained via an ontology graph, based on another visual analytics tool, called OntoVis. In this summary, we describe how these tools where used to analyze one of the mini-challenges of the 2008 VAST challenge.

An Incremental Layout Method for Visualizing Online Dynamic Graphs

Having the ability to draw dynamic graphs is key to better understanding evolving relationships and analyzing the patterns and trends in a network. Traditional force-directed methods are not suitable for laying out dynamic graphs because of their design for static graphs. An alternative is to create an incremental version of the force multilevel multi-pole method (FM3); however, previous solutions are more susceptible to graph degradation, that is, graph illegibility due to long edges or edge crossings. This is typically caused when distant components are connected, resulting in long and overlapping edges. We present our incremental version of FM3 with a refinement scheme, which solves this problem by “refining” the parts of the graph with high energy. Our resulting visualization maintains readability of the graph structure and is efficient in laying out these changing networks. We evaluate the effectiveness of our method by comparing it with two previous online dynamic graph methods.

A system for visual analysis of radio signal data

Analysis of radio transmissions is vital for military defense as it provides valuable information about enemy communication and infrastructure. One challenge to the data analysis task is that there are far too many signals for analysts to go through by hand. Even typical signal meta data (such as frequency band, duration, and geographic location) can be overwhelming. In this paper, we present a system for exploring and analyzing such radio signal meta-data. Our system incorporates several visual representations for signal data, designed for readability and ease of comparison, as well as novel algorithms for extracting and classifying consistent signal patterns. We demonstrate the effectiveness of our system using data collected from real missions with an airborne sensor platform.