Journal articles on the topic 'Node-link diagram'

Visual representations of node-link diagrams are very important for the software development process. In many situations, large diagrams have to be explored, whereby diagram elements of interest are often clipped from the viewport and are therefore not visible. Thus, in state-of-the-art modeling tools, navigation is accompanied by time-consuming panning and zooming. One solution to this problem is offscreen visualization techniques. Usually, they indicate the existence and direction of clipped elements by overlays at the border of the viewport. In this article, we contribute the application of offscreen visualization techniques to the domain of node-link diagrams in general and to Unified Modeling Language class diagrams in particular. The basic idea of our approach is to represent offscreen nodes by proxy elements located within an interactive border region around the viewport. The proxies show information of the associated offscreen nodes and can be used to quickly navigate to the respective node. In addition, we contribute techniques that preserve the routing of edges during panning and zooming and present strategies to make our approach scalable to large diagrams. We conducted a formative pilot study of our first prototype. Based on the observations made during the evaluation, we suggest how particular techniques should be combined. Finally, we ran a user evaluation to compare our technique with a traditional zoom+pan interface. The results showed that our approach is significantly faster for exploring relationships within diagrams than state-of-the-art interfaces. We also found that the offscreen visualization combined with an additional overview window did not improve the orientation within an unknown diagram. However, an overview should be offered as a cognitive support. CR categories: D.2.2 [Software Engineering]: Design Tools and Techniques— User Interface; H.5.2 [Information Interfaces and Presentation]: User Interfaces— Graphical User Interfaces General terms: Design, Human Factors

For reasons of clarity, a typical node–link diagram statically displayed on paper or a computer screen contains fewer than 30 nodes. However, many problems would benefit if far more complex information could be diagrammed. Following Munzner et al., we suggest that with interactive diagrams this may be possible. We describe an interactive technique whereby a subset of a larger network diagram is highlighted by being set into oscillatory motion when a node is selected with a mouse. The subset is determined by a breadth first search of the underlying graph starting from the selected node. This technique is designed to support visual queries on moderately large node-link diagrams containing up to a few thousand nodes. An experimental evaluation was carried out with networks having 32, 100, 320, 1000, and 3200 nodes respectively, and with four highlighting techniques: static highlighting, motion highlighting, static+ motion highlighting, and none. The results show that the interactive highlighting methods support rapid visual queries of nodes in close topological proximity to one another, even for the largest diagrams tested. Without highlighting, error rates were high even for the smallest network that was evaluated. Motion highlighting and static highlighting were equally effective. A second experiment was carried out to evaluate methods for showing two subsets of a larger network simultaneously in such a way that both are clearly distinct. The specific task was to determine if the two subsets had nodes in common. The results showed that this task could be performed rapidly and with few errors if one subset was highlighted using motion and the other was highlighted using a static technique. We discuss the implications for information visualization.

The reliability of the Internet of Things (IoT) system is analyzed and studied through ordered binary decision diagram (OBDD) to improve its design, application, and development. Based on the OBDD analysis, a reliability evaluation method named as enhanced node expansion (ENE) is proposed. This method provides an effective solution for the reliability assessment of IoT with large scale and complex network structure. A link importance assessment method based on OBDD analysis is also established. The proposed method can accurately and effectively evaluate the reliability of the IoT network and is practical for discussing the reliability, design, and development of this system.

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.

This paper describes a prototype called CoMem (Corporate Memory) that supports the finding and understanding of useful items in large hierarchical repositories. The particular domain is civil engineering design, and the prototype is designed specifically to support design reuse in building construction projects. However, the underlying visualization and interaction principals behind CoMem are generalizable to the ubiquitous task of finding and understanding useful information in large hierarchical repositories. To support the finding, the entire hierarchy is visualized using a squarified treemap. Once an item from the treemap is selected, CoMem supports the understanding of that item by identifying related items in the hierarchy and visualizing the selected item in the context of these related items in a node-link diagram. The paper concludes with a brief discussion of a usability evaluation of CoMem that supports the claim that finding and understanding improve the process of reuse, and that the described visualizations assist with finding and understanding.

This paper describes a new approach, space-optimized tree, for the visualization and navigation of tree-structured relational data. This technique can be used especially for the display of very large hierarchies in a two-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limited screen resolution. Space-optimized tree is a connection+ enclosure visualization approach that recursively positions children of a subtree into polygon areas and still uses a node–link diagram to present the entire hierarchical structure. To be able to handle the navigation of large hierarchies, we use a new hybrid viewing technique that combines two viewing methods, the modified semantic zooming and a focus+ context technique. While the semantic zooming technique can enlarge a particular viewing area by filtering out the rest of tree structure from the visualization, the focus+context technique allows the user to interactively focus, view and browse the entire visual structure with a reasonable high-density display.

In this article, we describe a taxonomy of generic graph related tasks along with a computer-based evaluation designed to assess the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation encompasses seven generic tasks and leads to insightful recommendations for the representation of graphs according to their size and density. Typically, we show that when graphs are bigger than twenty vertices, the matrix-based visualization outperforms node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation.

Adjacency matrices or DSMs (design structure matrices) and node-link diagrams are both visual representations of graphs, which are a common form of data in many disciplines. DSMs are used throughout the engineering community for various applications, such as process modelling or change prediction. However, outside this community, DSMs (and other matrix-based representations of graphs) are rarely applied and node-link diagrams are very popular. This paper will examine, which representation is more suitable for visualising graphs. For this purpose, several user experiments were conducted that aimed to answer this research question in the context of product models used, for example in engineering, but the results can be generalised to other applications. These experiments identify key factors on the readability of graph visualisations and confirm work on comparisons of different representations. This study widens the scope of readability comparisons between node-link and matrix-based representations by introducing new user tasks and replacing simulated, undirected graphs with directed ones employing real-world semantics.

Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.

Color is a widely used visual channel for encoding data in visualization design. It is important to select the appropriate type of color mapping to better understand the data. While several studies have investigated the effects of colormaps in various types of information visualization, there have been no studies on their effects on network visualization. Thus, in this paper, we investigate the effects of several colormaps in network visualization using node-link diagrams. Specifically, we compare four different single- and multi-hue colormaps for node attributes, and evaluate their effectiveness in terms of task completion time and correctness rate. Our results show that participants complete their tasks significantly faster with blue (single-hue, sequential) as compared to viridis (multi-hue, sequential), RdYlBu (divergent, red-yellow-blue), and jet (rainbow) colormaps. Additionally, the overall correctness rate shows significant differences between colormaps, with viridis being the least error-prone among the colormaps studied.

Radial, space-filling (RSF) techniques for hierarchy visualization have several advantages over traditional node–link diagrams, including the ability to use the display space efficiently while effectively conveying the hierarchy structure. Several RSF systems and tools have been developed to date, each with varying degrees of support for interactive operations such as selection and navigation. In this paper, we describe what we believe to be a complete set of desirable operations on hierarchical structures. We then present InterRing, an RSF hierarchy visualization system that supports a significantly more extensive set of these operations than prior systems. In particular, InterRing supports multifocus distortions, interactive hierarchy reconfiguration, and both semiautomated and manual selection. We show the power and utility of these and other operations, and describe our ongoing efforts to evaluate their effectiveness and usability.

A large class of diagrams can be informally characterized as node–link diagrams. Typically nodes represent entities, and links represent relationships between them. The discipline of graph drawing is concerned with methods for drawing abstract versions of such diagrams. At the foundation of the discipline are a set of graph aesthetics (rules for graph layout) that, it is assumed, will produce graphs that can be clearly understood. Examples of aesthetics include minimizing edge crossings and minimizing the sum of the lengths of the edges. However, with a few notable exceptions, these aesthetics are taken as axiomatic, and have not been empirically tested. We argue that human pattern perception can tell us much that is relevant to the study of graph aesthetics including providing a more detailed understanding of aesthetics and suggesting new ones. In particular, we find the importance of good continuity (ie keeping multi-edge paths as straight as possible) has been neglected. We introduce a methodology for evaluating the cognitive cost of graph aesthetics and we apply it to the task of finding the shortest paths in spring layout graphs. The results suggest that after the length of the path the two most important factors are continuity and edge crossings, and we provide cognitive cost estimates for these parameters. Another important factor is the number of branches emanating from nodes on the path.

The research scope in concept mapping research can be extended with the investigation of complementary concept mapping approaches, which are visual representations that complement the classical node link diagrams from Novak. To do so, concept mapping researchers can draw inspiration from architects. This article presents four strategies of architects to map concepts. The analysis shows what concept mapping researchers can benefit from architects: first, from the practice of architects to use complementary visualizations, which is investigated in the research field of Knowledge Visualization. Second, from their practice to structure information, which is investigated in the research field of Information Architecture. Both research fields are relevant for concept mapping researchers who investigate new formal approaches that complement the concept mapping approach taken by Novak. Third, the article discusses examples of complementary concept maps in different business situations.

This study used a between-subjects experimental design to examine the effects of three different computer-based instructional strategies (concept map, refutation text, and expository scientific text) on science learning. Concept maps are node-link diagrams that show concepts as nodes and relationships among the concepts as labeled links. Refutational texts are designed specifically to elicit common misconceptions that learners typically hold about a particular topic, directly refute the misconceptions, and present scientific explanations as compelling alternatives. Expository scientific texts consist of texts that are written specifically to present correct scientific information. Sixty-seven participants were randomly assigned to study one of three computer-based presentations on climate change. The dependent measures were tests of free recall and transfer. The concept map group significantly outperformed the refutational and scientific text groups on both free recall and transfer tests. Practical and theoretical implications of these findings are discussed.

Cascades appear in many applications, including biological graphs and social media analysis. In a cascade, a dynamic attribute propagates through a graph, following its edges. We present the results of a formal user study that tests the effectiveness of different types of cascade visualisations on node-link diagrams for the task of judging cascade spread. Overall, we found that a small multiples presentation was significantly faster than animation with no significant difference in terms of error rate. Participants generally preferred animation over small multiples and a hierarchical layout to a force-directed layout. Considering each presentation method separately, when comparing force-directed layouts to hierarchical layouts, hierarchical layouts were found to be significantly faster for both presentation methods and significantly more accurate for animation. Representing the history of the cascade had no significant effect. Thus, for our task, this experiment supports the use of a small multiples interface with hierarchically drawn graphs for the visualisation of cascades. This work is important because without these empirical results, designers of dynamic multivariate visualisations (in many applications) would base their design decisions on intuition with little empirical support as to whether these decisions enhance usability.

Purpose The usability aspect of the construction operations building information exchange (COBie) datasheet has been largely overlooked. Users find it difficult to find relevant data inside COBie and understand the dependencies of information. This research study is a part of a more comprehensive research study to identify the usability issues associated with COBie and propose solutions to deal with them. This paper aims to discuss the challenges associated with the visualization aspect of COBie and proposes a solution to mitigate them. Design/methodology/approach This paper is based on design thinking and waterfall methodology. While the design thinking methodology is used to explore the issues associated with the visualization aspect of COBie, the waterfall methodology is used to develop a working prototype of the visualizer for the COBie datasheet using a spreadsheet format. Findings The paper demonstrates that the property graph model based on a node-link diagram can be effectively used to represent the COBie datasheet. This will help in storing data in a visually connected manner and looking at links more dynamically. Moreover, converting and storing data into an appropriate database will help reach data directly rather than navigate multiple workbooks. This database can also help get the history of data inside the COBie datasheet as it develops throughout the project. Originality/value This research proposes a novel approach to visualize the COBie datasheet interactively using the property graph model, a type of node-link diagram. Using the property graph model will help users see data in a connected way, which is currently missing in the spreadsheet representation of COBie data. Moreover, this research also highlights that storing historical changes in COBie data can help understand how data has evolved throughout the construction. Additionally, structured storage of data in relationship format can help users to access the end of connected data directly through the efficient search.

AbstractNetwork graphs are used for high-stake decision making in medical and other contexts. For instance, graph drawings conveying relatedness can be relevant in the context of spreading diseases. Node-link diagrams can be used to visually assess the degree of homophily in a network—a condition where a presence of the link is more likely when nodes are similar. In an online experiment (N = 531), we tested how robustly laypeople can judge homophily from node-link diagrams and how variation of time constraints and layout of the diagrams affect judgments. The results showed that participants were able to give appropriate judgments. While granting more time led to better performance, the effects were small. Rather, the first seconds account for most of the information an individual can extract from the graphs. Furthermore, we showed a difference in performance between two types of layouts (bipartite and polarized). Results have consequences for communicating the degree of homophily in network graphs to the public.

AbstractThe visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described. As the novelty of this technique, insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes. As an additional feature set, an automatic identification of groups, clusters, and outliers is provided over time, and based on the visual outcome of the node-link and matrix visualizations, the repertoire of the supported layout and matrix reordering techniques is extended, and more interaction techniques are provided when considering the dynamics of the graph data. Finally, a small user experiment was conducted to investigate the usability of the proposed approach. The usefulness of the proposed tool is illustrated by applying it to a graph dataset, such as e co-authorships, co-citations, and a Comprehensible Perl Archive Network distribution.

In the half-century since Shannon invented information theory… engineers have come up with brilliant ways of boiling redundancy out of information… This lets it be transmitted twice as fast (Bill Gates: 33). Shannon’s Code Puts an End to Noise The digital machine is often presented as the perfect medium for the efficient transmission of coded messages: an ever-improving machine, in which coded information travels near to the-speed-of-light. Integrated into a global network of communication, transmission is assumed to be friction-free – everything and everybody are just a click away. Indeed, the old problem of signal interference is subdued by the magnum opus of communication engineering – Shannon’s noiseless channel – a cure for the undesirable uncertainties of message sending (Shannon and Weaver 19). For that reason alone, the digitally enhanced fidelity of Shannon’s digital code, not only heralds a new age of communication, but also marks the end of the problem of noise. In effect, his mathematical theory of communication, establishes a highly effective coding mechanism that travels from sender to receiver, overcoming geographic constraint and the deafening raw of the analogue milieu. This makes the theory itself the substratum of the digital communication utopia, since Shannon’s conquest of noise has solved the reliability problem of code, allowing us to focus on the rapidity and fecundity of our messages. However, despite the ingenuity of the noiseless channel, its subsequent rapid expansion into a vast network of machines means that both senders and receivers pay a price for Shannon’s brilliance. The speed and boundless reproducibility of digital code outperforms our physical capacity to observe it. In this way, transmission works behind the scenes, becoming increasingly independent of the human gaze. Even so, we are assured that we will not be overwhelmed by code; a new digital order has purportedly emerged. As follows, network innovators provide us with robotic codes that work benevolently on our behalf, exploring a seemingly random universe of connection. These intelligent codes search the tangled webs that constitute digital communication networks, autonomously in step with our fleeting transactions and data desires. We can sleep safely at night… this is The Road Ahead. But of course, as we now know, the ideal system for perpetual communication has also turned out to be the perfect medium for the codes designed to destroy it (Gordon). Instead of efficiently taking care of our transmission needs, the flow of code has been interrupted by the relational interactions of a machinic assemblage (Deleuze and Guattari). This is a vast assemblage populated by both human and non-human actors. Its evolution has not followed a predictable path determined by the innovations of the science of code, but instead responds to the complex interactions and interconnectedness of the network environment. In this way, the binary switches of the robotic code have occasionally flipped over from true to false – from the munificent to the malevolent function. The interruption seems to be relatively new, but the human-computer assemblage has a long history of the production of what I term liar codes. They follow on from Gödel and Turing’s realisation of the incompleteness and undecidability of self-referential systems of logic in the 1930s. In the following decades, von Neumann’s ideas on self-reproducing code provided early programmers with the means to play coded games of life. Thirty years later and researchers discovered how unstable a network would become when a similarly coded evolutive got out of control (Shoch and Hupp, Cohen). By 1990, the digital worm had turned. Morris’s code famously ‘crashed’ the Internet. The liar code had escaped the research lab and entered the wild world of the network. Nevertheless, despite what appears to be the uncontrollable evolution of code, it is the assemblage itself that makes a difference. Many liar codes have since followed on from the games, experiments and accidents of the early human-computer assemblage. Some are simply mischievous pranks designed to take up space by making copies of themselves, while others conceal a deeper, sinister pre-programmed function of data piracy (Bey 401-434) and viral hijack. The former spread out across a network, spewing out fairly innocuous alerts, whereas the latter steel passwords, gaining access to safe places, capturing navigation tools, and redirecting our attention to the dark side of the global village. In addition to the deluge of spam, viruses and worms, liar code arrives hidden in Trojan programs. Like Russian dolls, code slips into email inboxes. Simple viral sentences repeatedly trick us into opening these programs and spreading the infection. By saying “I love you” code becomes a recursive deceiver, concealing the true intentions of the virus writer, while ensuring that the victim plays a crucial role in the propagation of the liar. Noise Is Dead – Long Live the New Noise! More recently Liar codes have been cunningly understood as contemporary instances of cultural noise – the other of order (Parikka). However, this does not mean that a solution can be found in the universality of Shannon’s linear diagram – this is an altogether different engineering problem. In principle, Shannon’s noise was more readily apprehended. It existed primarily at a technical level (signal noise), a problem solved by the incorporation of noise into a technical code (redundancy). Contrariwise, liar codes go beyond the signal/noise ratio of the content of a message. They are environmental absurdities and anomalies that resonate outside the technical layer into the cultural milieu of the human-computer assemblage. The new noise is produced by the hissing background distortion of the network, which relentlessly drives communication to a far-from-equilibrial state. Along these lines, the production of what appears to be a surplus of code is subject to the behaviour and functioning of a vast and vulnerable topology of human and non-human machinic interaction. Is the Solution to Be Found in a Clash of Codes? In an attempt to banish the network pirates and their growing phylum of liar codes there has been a mobilisation of antivirus technologies. Netizens have been drafted in to operate the malware blockers, set up firewalls or dig the electronic trenches. But these desperate tactics appeal only to those who believe that they can reverse the drift towards instability, and return a sense of order to the network. In reality, evidence of the effectiveness of these counter measures is negligible. Despite efforts to lower the frequency of attacks, the liar code keeps seeping in. Indeed, the disorder from which the new noise emerges is quite unlike the high entropic problem encountered by Shannon. Like this, digital anomalies are not simply undesirable, random distortions, repaired by coded negentropy. On the contrary, the liar is a calculated act of violence, but this is an action that emerges from a collective, war-like assemblage. Therefore, significantly, it is not the code, but the relational interactions that evolve. For this reason, it is not simply the liar codes that threaten the stability of transmission, but the opening-up of a networked medium that captures messages, turning them into an expression of the unknown of order. Code no longer conveys a message through a channel. Not at all, it is the assemblage itself that anarchically converts the message into an altogether different form of expression. The liar is a rhizome, not a root!! (See figure 1.) A Network Diagram of Senders, Receivers and Deceivers Rhizomatic liar code introduces an anarchic scrambling of the communication model. Ad nauseam, antivirus researchers bemoan the problem of the liar code, but their code-determined defence system has seemingly failed to tell apart the senders, receivers and deceivers. Their tactics cannot sidestep the Gödelian paradox. Interestingly, current research into complex network topologies, particularly the Internet and the Web (Barabási), appears to not only support this gloomy conclusion, but confirms that the problem extends beyond code to the dynamic formation of the network itself. In this way, complex network theory may help us to understand how the human-computer assemblage comes together in the production of viral anomalies. Indeed, the digital network is not, as we may think, a random universe of free arbitrary association. It does not conform to the principles leading to inevitable equilibrium (an averaging out of connectivity). It is instead, an increasingly auto-organised and undemocratic tangle of nodes and links in which a few highly connected aristocratic clusters form alongside many isolated regions. In this far-from-random milieu, the flow of code is not determined by the linear transmission of messages between senders and receivers, or for that matter is it guided by an algorithmic evolutive. On the contrary, while aristocratic networks provide a robust means of holding an assemblage together, their topological behaviour also makes them highly susceptible to viral epidemics. Liar codes easily spread through clusters formed out of preferential linkage, and a desire for exclusive, network alliances between humans and non-humans. From a remote location, a single viral code can promiscuously infect a highly connected population of nodes (Pastor-Satorras & Vespignani). This is the perfect environment for the actions of deceivers and their liar codes. On reflection, a revised diagram of transmission, which tackles head on the viral anomalies of the human-computer assemblage, would perhaps be unworkable. This is consistent with the problem of liar codes, and the resulting collapse of trustworthy transmission. The diagram would ideally need to factor in the anarchic, scrambled lines of communication (see figure 1), as well as the complex topological relations between node and link. Such a diagram would also need to trace significant topological behaviours and functions alongside the malfunctions of codes, coders and the sharing of codes over a network. It is this significant topological intensity of the human-computer assemblage that shifts the contemporary debate on noise away from Shannon’s model towards a complex, non-linear and relational interaction. In this sense, the diagram moves closer to the rhizomatic notion of a network (Deleuze and Guattari 9-10). Not so much a model of transmission, rather a model of viral transduction. 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Parikka, Jussi. “Viral Noise and the (Dis)Order of the Digital Culture.” M/C Journal 7.6 (2005). 5 Dec. 2005 http://journal.media-culture.org.au/0501/05-parikka.php>. Shannon, Claude, and Warren Weaver. The Mathematical Theory of Communication. University of Illinois Press, 1949/1998. Shoch, John F, and Jon A Hupp. “The ‘Worm’ Programs – Early Experience with a Distributed Computation.” Communications of the ACM 25.3 (March 1982): 172–180. 5 Dec. 2005. Pastor-Satorras, Romualdo, and Alessandro Vespignani. “Epidemic Spreading in Scale-Free Networks.” Physical Review Letters 86 (2001). Von Neumann, John, and Arthur Burks. Theory of Self-Reproducing Automata. University of Illinois Press, 1966. Citation reference for this article MLA Style Sampson, Tony. "Senders, Receivers and Deceivers: How Liar Codes Put Noise Back on the Diagram of Transmission." M/C Journal 9.1 (2006). echo date('d M. Y'); ?> <http://journal.media-culture.org.au/0603/03-sampson.php>. APA Style Sampson, T. (Mar. 2006) "Senders, Receivers and Deceivers: How Liar Codes Put Noise Back on the Diagram of Transmission," M/C Journal, 9(1). Retrieved echo date('d M. Y'); ?> from <http://journal.media-culture.org.au/0603/03-sampson.php>.

Abstract Adding temporal information to social network visualizations is still a challenging task despite previous research efforts. Visualizing call logs on an event-based level can show various attributes of a connection. The dimension time is of great interest to analysts as it offers insights into trends and patterns such as changing relationships between different actors or economic opportunities for businesses. Yet current approaches suffer from limitations that can be improved with the visualization design presented in this work. Our presented visualization was developed considering aesthetic criteria and characteristics of adjacency matrices and node-link diagrams. A heuristic evaluation according to these criteria was conducted. In a formative evaluation process, an artificial dataset was specifically created to examine dynamic social networks. A qualitative user study with observation and think-aloud protocols was conducted and analyzed with regard to the user’s strategies, limitations of the visualization and potential additional features. The visualization appears to be suitable for all of the evaluated network tasks; however, path-related tasks were more challenging than other tasks. Graphical abstract