Embedding Graphs under Centrality Constraints for Network Visualization

Visual rendering of graphs is a key task in the mapping of complex network data. Although most graph drawing algorithms emphasize aesthetic appeal, certain applications such as travel-time maps place more importance on visualization of structural network properties. The present paper advocates two graph embedding approaches with centrality considerations to comply with node hierarchy. The problem is formulated first as one of constrained multi-dimensional scaling (MDS), and it is solved via block coordinate descent iterations with successive approximations and guaranteed convergence to a KKT point. In addition, a regularization term enforcing graph smoothness is incorporated with the goal of reducing edge crossings. A second approach leverages the locally-linear embedding (LLE) algorithm which assumes that the graph encodes data sampled from a low-dimensional manifold. Closed-form solutions to the resulting centrality-constrained optimization problems are determined yielding meaningful embeddings. Experimental results demonstrate the efficacy of both approaches, especially for visualizing large networks on the order of thousands of nodes.Comment: Submitted to IEEE Transactions on Visualization and Computer Graphic

A layout algorithm for undirected compound graphs

Cataloged from PDF version of article.We present an algorithm for the layout of undirected compound graphs, relaxing restrictions of previously known algorithms in regards to topology and geometry. The algorithm is based on the traditional force-directed layout scheme with extensions to handle multi-level nesting, edges between nodes of arbitrary nesting levels, varying node sizes, and other possible application-specific constraints. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory. The algorithm has also been successfully implemented as part of a pathway integration and analysis toolkit named PATIKA, for drawing complicated biological pathways with compartmental constraints and arbitrary nesting relations to represent molecular complexes and various types of pathway abstractions. (C) 2008 Elsevier Inc. All rights reserved

Put three and three together: Triangle-driven community detection

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly capturing such notion. The main reason is that these metrics consider the internal community edges as a set, but ignore how these actually connect the vertices of the community. We propose the Weighted Community Clustering (WCC), which is a new community metric that takes the triangle instead of the edge as the minimal structural motif indicating the presence of a strong relation in a graph. We theoretically analyse WCC in depth and formally prove, by means of a set of properties, that the maximization of WCC guarantees communities with cohesion and structure. In addition, we propose Scalable Community Detection (SCD), a community detection algorithm based on WCC, which is designed to be fast and scalable on SMP machines, showing experimentally that WCC correctly captures the concept of community in social networks using real datasets. Finally, using ground-truth data, we show that SCD provides better quality than the best disjoint community detection algorithms of the state of the art while performing faster.Peer ReviewedPostprint (author's final draft

Algorithmique des réseaux socio-sémantiques pour la visualisation par points de vue des communautés en ligne

Within the community detection problem it is possible to use either the structural dimension or the composition dimension of the social network; on the first case the communities contain groups of well-connected and dissimilar nodes whereas on the second case, the communities contain groups of similar but loosely connected nodes. Therefore the amount of information extracted is reduced as one of the dimensions is discarded. The objective of this Thesis is to propose a novel approach for detecting communities in which the structural and composition dimensions are integrated in such a way the communities contain groups of well-connected and similar nodes. This approach requires first, a new definition of community that includes both dimensions of the network, then a new community detection model suited for this new definition that allows us to find groups of well-connected and similar nodes. The model starts introducing the notion of point of view that allows the division of the composition dimension for analyzing the network from different perspectives. Then the model influences the community detection process by integrating the composition information into the graph structure. The last step is the social network visualization that places the nodes according to their structural and compositional similarities and that allows us to find important nodes regarding the interaction between communities.Dans le problème de détection de communautés il est possible d'utiliser soit la dimension structurelle, soit la dimension compositionelle du réseau : dans le premier cas les communautés seraient composées par des groupes de noeuds fortement connectés mais peu similaires, et pour le deuxième cas, les groupes auraient des noeuds similaires mais faiblement connectés. Donc en ne choisissant qu'une des dimensions la quantité possible d'information à extraire est réduite. Cette thèse a pour objectif de proposer une nouvelle approche pour utiliser en même temps les dimensions structurelle et compositionelle lors de la détection de communautés de façon telle que les groupes aient des noeuds similaires et bien connectés. Pour la mise en oeuvre de cette approche il faut d'abord une nouvelle définition de communauté qui prend en compte les deux dimensions présentées auparavant et ensuite un modèle nouveau de détection qui utilise cette définition, en trouvant des groupes de noeuds similaires et bien connectés. Le modèle commence par l'introduction de la notion de point de vue qui permet de diviser la dimension compositionelle pour analyser le réseau depuis différentes perspectives. Ensuite le modèle, en utilisant l'information compositionelle, influence le processus de détection de communautés qui intègre les deux dimensions du réseau. La dernière étape est la visualisation du graphe de communautés qui positionne les noeuds selon leur similarité structurelle et compositionelle, ce qui permet d'identifier des noeuds importants pour les interactions entre communautés

Analysis and Visualisation of Edge Entanglement in Multiplex Networks

Cette thèse présente une nouvelle méthodologie pour analyser des réseaux. Nous développons l'intrication d'un réseau multiplex, qui se matérialise sous forme d'une mesure d'intensité et d'homogénéité, et d'une abstraction, le réseau d'interaction des catalyseurs, auxquels sont associés des indices d'intrication. Nous présentons ensuite la mise en place d'outils spécifiques pour l'analyse visuelle des réseaux complexes qui tirent profit de cette méthodologie. Ces outils présente une vue double de deux réseaux,qui inclue une un algorithme de dessin, une interaction associant brossage d'une sélection et de multiples liens pré-attentifs. Nous terminons ce document par la présentation détaillée d'applications dans de multiples domaines.When it comes to comprehension of complex phenomena, humans need to understand what interactions lie within them.These interactions are often captured with complex networks. However, the interaction pluralism is often shallowed by traditional network models. We propose a new way to look at these phenomena through the lens of multiplex networks, in which catalysts are drivers of the interaction through substrates. To study the entanglement of a multiplex network is to study how edges intertwine, in other words, how catalysts interact. Our entanglement analysis results in a full set of new objects which completes traditional network approaches: the entanglement homogeneity and intensity of the multiplex network, and the catalyst interaction network, with for each catalyst, an entanglement index. These objects are very suitable for embedment in a visual analytics framework, to enable comprehension of a complex structure. We thus propose of visual setting with coordinated multiple views. We take advantage of mental mapping and visual linking to present simultaneous information of a multiplex network at three different levels of abstraction. We complete brushing and linking with a leapfrog interaction that mimics the back-and-forth process involved in users' comprehension. The method is validated and enriched through multiple applications including assessing group cohesion in document collections, and identification of particular associations in social networks.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

An ontological approach to information visualization.

Visualization is one of the indispensable means for addressing the rapid explosion of data and information. Although a large collection of visualization techniques have been developed over the past three decades, the majority of ordinary users have little knowledge about these techniques. Despite there being many interactive visualization tools available in the public domain or commercially, producing visualizations remains a skilled and time-consuming task. One approach for cost-effective dissemination of visualization techniques is to use captured expert knowledge for helping ordinary users generate visualizations automatically. In this work, we propose to use captured knowledge in ontologies to reduce the parameter space, providing a more effective automated solution to the dissemination of visualization techniques to ordinary users. As an example, we consider the visualization of music chart data and football statistics on the web, and aim to generate visualizations automatically from the data. The work has three main contributions: Visualisation as Mapping. We consider the visualization process as a mapping task and assess this approach from both a tree-based and graph-based perspective. We discuss techniques for automatic mapping and present a general approach for Information Perceptualisation through mapping which we call Information Realisation. VizThis: Tree-centric Mapping. We have built a tree-based mapping toolkit which provides a pragmatic solution for visualising any XML-based source data using either SVG or X3D (or potentially any other XML-based target format). The toolkit has data cleansing and data analysis features. It also allows automatic mapping through a type-constrained system (AutoMap). If the user wishes to alter mappings, the system gives the users warnings about specific problem areas so that they can be immediately corrected. SeniViz: Graph-centric Mapping. We present an ontology-based pipeline to automatically map tabular data to geometrical data, and to select appropriate visualization tools, styles and parameters. The pipeline is based on three ontologies: a Domain Ontology (DO) captures the knowledge about the subject domain being visualized; a Visual Representation Ontology (VRO) captures the specific representational capabilities of different visualization techniques (e.g.. Tree Map); and a Semantic Bridge Ontology (SBO) captures specific expert-knowledge about valuable mappings between domain and representation concepts. In this way, we have an ontology mapping algorithm which can dynamically score and rank potential visualizations. We also present the results of a user study to assess the validity and effectiveness of the SemViz approach