1,741 research outputs found
On directed information theory and Granger causality graphs
Directed information theory deals with communication channels with feedback.
When applied to networks, a natural extension based on causal conditioning is
needed. We show here that measures built from directed information theory in
networks can be used to assess Granger causality graphs of stochastic
processes. We show that directed information theory includes measures such as
the transfer entropy, and that it is the adequate information theoretic
framework needed for neuroscience applications, such as connectivity inference
problems.Comment: accepted for publications, Journal of Computational Neuroscienc
Health Figures: An Open Source JavaScript Library for Health Data Visualization
The way we look at data has a great impact on how we can understand it,
particularly when the data is related to health and wellness. Due to the
increased use of self-tracking devices and the ongoing shift towards preventive
medicine, better understanding of our health data is an important part of
improving the general welfare of the citizens. Electronic Health Records,
self-tracking devices and mobile applications provide a rich variety of data
but it often becomes difficult to understand. We implemented the hFigures
library inspired on the hGraph visualization with additional improvements. The
purpose of the library is to provide a visual representation of the evolution
of health measurements in a complete and useful manner. We researched the
usefulness and usability of the library by building an application for health
data visualization in a health coaching program. We performed a user evaluation
with Heuristic Evaluation, Controlled User Testing and Usability
Questionnaires. In the Heuristics Evaluation the average response was 6.3 out
of 7 points and the Cognitive Walkthrough done by usability experts indicated
no design or mismatch errors. In the CSUQ usability test the system obtained an
average score of 6.13 out of 7, and in the ASQ usability test the overall
satisfaction score was 6.64 out of 7. We developed hFigures, an open source
library for visualizing a complete, accurate and normalized graphical
representation of health data. The idea is based on the concept of the hGraph
but it provides additional key features, including a comparison of multiple
health measurements over time. We conducted a usability evaluation of the
library as a key component of an application for health and wellness
monitoring. The results indicate that the data visualization library was
helpful in assisting users in understanding health data and its evolution over
time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016
Inferring Multiple Graphical Structures
Gaussian Graphical Models provide a convenient framework for representing
dependencies between variables. Recently, this tool has received a high
interest for the discovery of biological networks. The literature focuses on
the case where a single network is inferred from a set of measurements, but, as
wetlab data is typically scarce, several assays, where the experimental
conditions affect interactions, are usually merged to infer a single network.
In this paper, we propose two approaches for estimating multiple related
graphs, by rendering the closeness assumption into an empirical prior or group
penalties. We provide quantitative results demonstrating the benefits of the
proposed approaches. The methods presented in this paper are embeded in the R
package 'simone' from version 1.0-0 and later
How analysts think: sense-making strategies in the analysis of temporal evolution and criminal network structures and activities
Analysis of criminal activity based on offenders’ social networks is an established procedure in intelligence analysis. The complexity of the data poses an obstacle for analysts to gauge network developments, e.g. detect emerging problems. Visualization is a powerful tool to achieve this, but it is essential to know how the analysts’ sense-making strategies can be supported most efficiently. Based on a think aloud study we identified ten cognitive strategies on a general level to be useful for designers. We also provide some examples how these strategies can be supported through appropriate visualizations
Sequence analysis: its past, present, and future
This article marks the occasion of Social Science Research’s 50th anniversary by reflecting on the
progress of sequence analysis (SA) since its introduction into the social sciences four decades ago,
with focuses on the developments of SA thus far in the social sciences and on its potential future
directions.
The application of SA in the social sciences, especially in life course research, has mushroomed
in the last decade and a half. Using a life course analogy, we examined the birth of SA in the social
sciences and its childhood (the first wave), its adolescence and young adulthood (the second
wave), and its future mature adulthood in the paper.
The paper provides a summary of (1) the important SA research and the historical contexts in
which SA was developed by Andrew Abbott, (2) a thorough review of the many methodological
developments in visualization, complexity measures, dissimilarity measures, group analysis of
dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic
SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social
science research, and software for SA, and (3) reflections on some future directions of SA
including how SA can benefit and inform theory-making in the social sciences, the methods
currently being developed, and some remaining challenges facing SA for which we do not yet
have any solutions. It is our hope that the reader will take up the challenges and help us improve
and grow SA into maturity
EXPLORATORY VISUALIZATION OF GRAPHS BASED ON COMMUNITY STRUCTURE
Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding complex systems, is a challenging yet important research topic. My work has been focused on visualization approaches to exploring the community structure in graphs based on automatic community detection results.
In this dissertation, we first report a formal user study that investigated the essen- tial influence factors, benefits, and constraints of a community based graph visual- ization system in a background application of seeking information from text corpora. A general evaluation methodology for exploratory visualization systems has been proposed and practiced. The evaluation methodology integrates detailed cognitive load analysis and users’ prior knowledge evaluation with quantitative and qualitative measures, so that in-depth insights can be gained. The study revealed that visual exploration based on the community structure benefits the understanding of real net- works. A literature review and a set of interviews were then conducted to learn tasks facing such graph exploration and the state-of-the-arts. This work led to commu- nity related graph visualization task taxonomy. Our examination of existing graph visualization systems revealed that a large number of community related graph visualization tasks are poorly supported in existing approaches. To bridge the gap, several
novel visualization techniques are proposed. In these approaches, graph topology information is mapped to a multidimensional space where the relationships between the communities and the nodes can be explicitly explored. Several user studies and case studies have been conducted to demonstrate the usefulness of these systems in real-world applications
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