Visualizing social networks, especially an overview emphasizing their structure, i.e., communities and their interconnections, is known to be a challenging problem. In this paper, we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper. We evaluate its performances against two of the most wide-spread visualization techniques (matrix and node-link diagram) in a human–computer controlled experiment based on community-related tasks. While none of the techniques emerge as the overwhelming winner, Jasper appears to be one of the best methods for each task; a fact sustained by the marks given by the users. Overall, Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.

Abstract Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

Advances in multimedia analysis, enables access and indexing of huge and rich video content. However, delivering this heterogeneous content remains a challenge. We proposes the use of multiplex networks to combine both textual and visual semantic cues extracted from TV news videos for interactive search refinement on heterogeneous data. After preprocessing, the indexed videos can be queried, for which result space is summarized in a visual cloud helping query refinement with contextual information by combining visual and textual cues. We leverage on properties of multiplex networks to extract a hierarchy of layers, which not only contextualizes results, but also guides the construction of our visual cloud, and its interactions.

One task key to the analysis of large multimedia archive over time is to dynamically monitor the activity of concepts and entities with their interactions. This is helpful to analyze threads of topics over news archives (how stories unfold), or to monitor evolutions and development of social groups. Dynamic graph modeling is a powerful tool to capture these interactions over time, while visualization and finding communities still remain difficult, especially with a high density of links. We propose to extract the backbone of dynamic graphs in order to ease community detection and guide the exploration of trends evolution. Through the graph structure, we interactively coordinate node-link diagrams, Sankey diagrams, time series, and animations in order to extract patterns and follow community behavior. We illustrate our system with the exploration of the role of soccer in 6 years of TV/radio magazines in France, and the role of North Korea in about 10 years of Japanese news.

Multimedia data is by nature heterogeneous, conveying semantic information through multiple cues. Text analysis of closed captions already brought us understanding of the spoken information. Today's advances in computer vision now enable us to look for relevant semantic information from the visual content of real-world archives. Combining these two levels of extracted information to make sense of an archive still remains a challenge. Multiplex net- works, which model multiple families of interactions in a graph, can capture and combine both sources of semantics. We can leverage on these objects to extract hierarchies and integrate them in an interactive heterogeneous "visual cloud". Inspired by word clouds, these clouds allow to grasp visual and textual semantic information captured from a multimedia collection all at once. The interaction then enables direct access to the relevant video. We demonstrate our system with the exploration of a Japanese news archive.

Abstract A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.

Software designs are ever changing to adapt to new environments and requirements. Tracking and understanding changes in modules and relationships in a software project is difficult, but even more so when the software goes through several types of changes. The typical complexity and size of software also makes it harder to grasp software evolution patterns. In this paper, we present an interactive matrix-based visualization technique that, combined with animation, depicts how software designs evolve. For example, it shows which new modules and couplings are added and removed over time. Our generic visualization supports dynamic and weighted digraphs and is applied in the context of software evolution. Analyzing source code changes is important to determine the software's structural organization and identify quality issues over time. To demonstrate our approach, we explore open-source repositories and discuss some of our findings regarding these evolving software designs.

The analysis and exploration of a social network depends on the type of relations at play. Borgatti had proposed a type taxonomy organizing relations in four possible categories. Homophily (similarity) relationships form an important category where relations occur when entities of the network link whenever they exhibit similar behaviors. Examples are networks of coauthor, where homophily between two persons follows from co-authorship; or network of actors having played under the supervision of the same movie director, for instance. Homophily is often embodied through a bipartite network where entities of a given type A (authors, movie directors) connect through entities of a different type B (papers, actors). A common strategy is then to project this bipartite graph onto a single-type network with entities of a same type A, possibly weighting edges based on how the type A entities interact with the type B entities underlying the edge. The resulting single-type network can then be studied using standard techniques such as community detection using edge density, or the computation of various centrality indices. This paper revisits this type of approach and introduces a homogeneity measure inspired form past work by Burt. Two entities of type B interact when they both induce a same edge between two entities of type A. The homogeneity of a subgroup thus depends on how intensely and how equally interactions occur between entities of type B giving rise to the subgroup. The measure thus differentiates between subgroups of type A exhibiting similar topologies depending on the interaction patterns of the underlying entities of type B. The method is validated using two widely used datasets. A first example looks at authors of the IEEE InfoVis Conference (InfoVis 2007 Contest). A second example looks at homophily relations between movie actors that have played under the direction of a same director (IMDB).

Exploring document collections remains a focus of research. This task can be tackled using various techniques, typically ranking documents according to a relevance index or grouping documents based on various clustering algorithms. The task complexity produces results of varying quality that inevitably carry noise. Users must be careful when interpreting document relevance or groupings. We address this problem by computing cohesion measures for a group of documents confirming/infirming whether it can be trusted to form a semantically cohesive unit. The index is inspired from past work in social network analysis (SNA) and illustrates how document exploration can benefit from SNA techniques.

Abstract Graph rewriting systems (GRSs) operate on graphs by substituting local patterns according to a set of rewriting rules. The apparent simplicity of GRSs hides an incredible complexity and turns the study of these systems into an involved task requiring high‐level expertise. We designed PORGY, an interactive visual environment to fully support GRSs related tasks, exploiting a long historical tradition of GRSs with node‐link representations of graphs. PORGY enables rule‐based modeling and simulation steering through graphical representations and direct manipulation of all GRSs components. This paper contributes a design study and task taxonomy relevant to the interactive visualization of GRSs.

This paper describes an approach exploiting the full capabilities of GPU's to enhance the usability of edge bundling in real applications. Edge bundling, as well as other edge clustering approaches relying on the use of high quality edge rerouting. Typical approach for drawing edge-bundled graph is to render edges as curves. But curves generation can have a relatively high computational costs and do not easily comply with real-time interaction. Furthermore, while edge bundling provides a much better overall readability of a graph, the bundles make it more difficult to recover local information. Our goal was thus to provide fluid interaction allowing the recovery of local information through specific interaction techniques. The system we built offers folklore or classical interaction such as zoom & pan, fish-eye and magnifying lens. We also implemented the Bring & Go technique by Tominski et al. We proposed an approach exploiting the full computing power of GPU's when rendering graph edges as parametric splines. The gain in efficiency when running all curves computations on the GPU turns bundling techniques into techniques that can be embedded in interactive systems concerned with graphs of several thousands of nodes and edges.

In this paper, we present a Hierarchical Fuzzy Clustering algorithm which uses domain knowledge to automatically determine the number of clusters and their initial values. The algorithm is applied on a collection of web pages and the results are compared with existing algorithms in the literature.

The emergence of scale free and small world properties in real world complex networks has stimulated lots of activity in the field of network analysis. An example of such a network comes from the field of Content Analysis (CA) and Text Mining where the goal is to analyze the contents of a set of web pages. The Network can be represented by the words appearing in the web pages as nodes and the edges representing a relation between two words if they appear in a document together. In this paper we present a CA system that helps users analyze these networks representing the textual contents of a set of web pages visually. Major contributions include a methodology to cluster complex networks based on duplication of nodes and identification of bridges i.e. words that might be of user interest but have a low frequency in the document corpus. We have tested this system with a number of data sets and users have found it very useful for the exploration of data. One of the case studies is presented in detail which is based on browsing a collection of web pages on Wikipedia.

Visual graph analytics definitely relies on the use of node and edge metrics to identify salient properties in graphs. Most often, these metrics are turned into useful visual cues, or are used to interactively filter out parts of a graph while querying it, for instance. Along the years, analysts coming from different application domains have designed metrics to serve specific needs. Graph analytics, sometimes also called network science, recently developed as a cross-discipline field developing models shared by numerous application domains such as bio-informatics, social network analysis, web graphs, etc.[4] [10]. As a consequence, we end up finding various metrics in the literature aiming at similar goals; different names and analytics description often hide similarity between two metrics that originated from different fields. We survey a list of edge metrics for graphs and compare their relative value and behaviour, in an effort to organize them into a taxonomy and underline the genuine ingredients in each of them disregarding their origin.

This paper introduces a new technique easing the navigation and interactive exploration of huge multidimensional datasets. Following the pixel-oriented paradigm [8], the key ingredients enabling the interactive navigation of extreme volumes of data rely on a set of functions bijectively mapping data elements to screen pixels. The use of the mapping from data elements to pixels constrain the computational complexity for the rendering process to be linear with respect to the number of rendered pixels on the screen as opposed to the dataset size. Our method furthermore allows the implementation of usual information visualization techniques such as zoom and pan, anamorphosis and texturing. As a proof-of-concept, we show how our technique can be adapted to interactively explore the Datacube, turning our approach into an efficient system for visual datamining. We report experiments conducted on a Datacube containing 50 millions of items. To our knowledge, our technique outperforms all existing ones and push the scalability limit close to the billion of elements. Supporting all basic navigation techniques, and being moreover flexible makes it easily reusable for a large number of applications.

This paper presents a novel technique for exploring and navigating large hierarchies, not restricted to trees but to directed acyclic graphs (DAGs). The technique combines two different visualizations emphasizing different points of view the user can adopt when analyzing the content of the hierarchy. Usual hierarchical (node-link) layout reflect the relative position of nodes in the hierarchy while a variation of a treemap emphasizes node attributes. The classical treemap algorithm has been adapted in order to deal with DAGs. Linking the two views enables the user to visually and interactively explore elements of the hierarchy with respect to selected attributes, while being able to locate the node in the DAG.

We present a multiscale MDS method extending Chalmers' pivot-based MDS algorithm (Morrison et al., 2003). Our multi-scale strategy is itself based on a 0(N log N) hybrid MDS approach. Our algorithm clearly improves over its predecessors with respect to time, while producing layouts of a comparable quality.

Abstract Many applications, from everyday file system browsers to visual programming tools, require the display of network and graph structures. The Graph Visualization Framework (GVF) (available at http://www.cwi.nl/InfoVisu/GVF) is an architecture that supports the tasks common to most graph browsers and editors. This article gives a brief overview of the design of the GVF and focuses on the core classes that are used to represent and manipulate graphs. The design of the core classes is justified by the requirements for navigation and visualization. Copyright © 2001 John Wiley & Sons, Ltd.

This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as Web browsing, state-transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective.

Information visualisation often requires good navigation aids on large trees, which represent the underlying abstract information. Using trees for information visualisation requires novel user interface techniques, visual clues, and navigational aids. This paper describes a visual clue: using the so‐called Strahler numbers, a map is provided that indicates which parts of the tree are interesting. A second idea is that of “folding” away subtrees that are too “different” in some sense, thereby reducing the visual complexity of the tree. Examples are given demonstrating these techniques, and what the further challenges in this area are.