162,997 research outputs found
Combining Text and Graphics for Interactive Exploration of Behavior Datasets
Modern sensor technologies and simulators applied to large and complex dynamic systems (such as road traffic networks, sets of river channels, etc.) produce large amounts of behavior data that are difficult for users to interpret and analyze. Software tools that generate presentations combining text and graphics can help users understand this data. In this paper we describe the results of our research on automatic multimedia presentation generation (including text, graphics, maps, images, etc.) for interactive exploration of behavior datasets. We designed a novel user interface that combines automatically generated text and graphical resources. We describe the general knowledge-based design of our presentation generation tool. We also present applications that we developed to validate the method, and a comparison with related work
Interactive Time-Series of Measures for Exploring Dynamic Networks
International audienceWe present MeasureFlow, an interface to visually and interactively explore dynamic networks through time-series of network measures such as link number, graph density, or node activation. When networks contain many time steps, become large and more dense, or contain high frequencies of change, traditional visualizations that focus on network topology, such as animations or small multiples , fail to provide adequate overviews and thus fail to guide the analyst towards interesting time points and periods. Measure-Flow presents a complementary approach that relies on visualizing time-series of common network measures to provide a detailed yet comprehensive overview of when changes are happening and which network measures they involve. As dynamic networks undergo changes of varying rates and characteristics, network measures provide important hints on the pace and nature of their evolution and can guide an analysts in their exploration; based on a set of interactive and signal-processing methods, MeasureFlow allows an analyst to select and navigate periods of interest in the network. We demonstrate MeasureFlow through case studies with real-world data
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
TopExNet: Entity-Centric Network Topic Exploration in News Streams
The recent introduction of entity-centric implicit network representations of
unstructured text offers novel ways for exploring entity relations in document
collections and streams efficiently and interactively. Here, we present
TopExNet as a tool for exploring entity-centric network topics in streams of
news articles. The application is available as a web service at
https://topexnet.ifi.uni-heidelberg.de/ .Comment: Published in Proceedings of the Twelfth ACM International Conference
on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February
11-15, 201
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
Visualization of Metabolic Networks
The metabolism constitutes the universe of biochemical reactions taking place in
a cell of an organism. These processes include the synthesis, transformation, and
degradation of molecules for an organism to grow, to reproduce and to interact
with its environment. A good way to capture the complexity of these processes
is the representation as metabolic network, in which sets of molecules are transformed
into products by a chemical reaction, and the products are being processed
further. The underlying graph model allows a structural analysis of this network
using established graphtheoretical algorithms on the one hand, and a visual representation
by applying layout algorithms combined with information visualization
techniques on the other.
In this thesis we will take a look at three different aspects of graph visualization
within the context of biochemical systems: the representation and interactive
exploration of static networks, the visual analysis of dynamic networks, and the
comparison of two network graphs. We will demonstrate, how established infovis
techniques can be combined with new algorithms and applied to specific problems
in the area of metabolic network visualization.
We reconstruct the metabolic network covering the complete set of chemical reactions
present in a generalized eucaryotic cell from real world data available from
a popular metabolic pathway data base and present a suitable data structure. As
the constructed network is very large, it is not feasible for the display as a whole.
Instead, we introduce a technique to analyse this static network in a top-down
approach starting with an overview and displaying detailed reaction networks on
demand. This exploration method is also applied to compare metabolic networks
in different species and from different resources. As for the analysis of dynamic
networks, we present a framework to capture changes in the connectivity as well
as changes in the attributes associated with the network’s elements
Improving democratic governance through institutional design: civic participation and democratic ownership in Europe
In this article we provide a conceptual and argumentative framework for studying how institutional design can enhance civic participation and ultimately increase citizens’ sense of democratic ownership of governmental processes. First, we set out the socio-political context for enhancing the democratic governance of regulatory policies in Europe, and highlight the way in which civic participation and democratic ownership is given equal weight to economic competitiveness. We then discuss the potential for institutionalised participatory governance to develop and their prospects for improving effective and democratic governance in the multi-layered European polity. The article concludes by outlining a research agenda for the field and identifying the priorities for scholars working in interaction with civil society and governments
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