25,343 research outputs found
Improving VANET Protocols via Network Science
Developing routing protocols for Vehicular Ad Hoc Networks (VANETs) is a
significant challenge in these large, self- organized and distributed networks.
We address this challenge by studying VANETs from a network science perspective
to develop solutions that act locally but influence the network performance
globally. More specifically, we look at snapshots from highway and urban VANETs
of different sizes and vehicle densities, and study parameters such as the node
degree distribution, the clustering coefficient and the average shortest path
length, in order to better understand the networks' structure and compare it to
structures commonly found in large real world networks such as small-world and
scale-free networks. We then show how to use this information to improve
existing VANET protocols. As an illustrative example, it is shown that, by
adding new mechanisms that make use of this information, the overhead of the
urban vehicular broadcasting (UV-CAST) protocol can be reduced substantially
with no significant performance degradation.Comment: Proceedings of the 2012 IEEE Vehicular Networking Conference (VNC),
Korea, November 201
Revisiting Interval Graphs for Network Science
The vertices of an interval graph represent intervals over a real line where
overlapping intervals denote that their corresponding vertices are adjacent.
This implies that the vertices are measurable by a metric and there exists a
linear structure in the system. The generalization is an embedding of a graph
onto a multi-dimensional Euclidean space and it was used by scientists to study
the multi-relational complexity of ecology. However the research went out of
fashion in the 1980s and was not revisited when Network Science recently
expressed interests with multi-relational networks known as multiplexes. This
paper studies interval graphs from the perspective of Network Science
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
The knowledge domain of chain and network science
This editorial paper aims to provide a framework to categorise and evaluate the domain of Chain and Network Science (CNS), and to provide an envelope for the research and management agenda. The authors strongly feel that although considerable progress has been made over the past couple of years in the development of the CNS domain, a number of important and exciting challenges are still waiting to be tackled. This paper provides a definition of the object of study of CNS, its central problem area, the organisation and governance of chain and network co-operation, and the relationships between chain organisation and technology development, market dynamics, and the economy and society at large. It indicates relevant sources of knowledge among the various academic disciplines. It touches upon CNS problem solving by identifying areas for knowledge development and CNS tool construction
Mapping the Curricular Structure and Contents of Network Science Courses
As network science has matured as an established field of research, there are
already a number of courses on this topic developed and offered at various
higher education institutions, often at postgraduate levels. In those courses,
instructors adopted different approaches with different focus areas and
curricular designs. We collected information about 30 existing network science
courses from various online sources, and analyzed the contents of their syllabi
or course schedules. The topics and their curricular sequences were extracted
from the course syllabi/schedules and represented as a directed weighted graph,
which we call the topic network. Community detection in the topic network
revealed seven topic clusters, which matched reasonably with the concept list
previously generated by students and educators through the Network Literacy
initiative. The minimum spanning tree of the topic network revealed typical
flows of curricular contents, starting with examples of networks, moving onto
random networks and small-world networks, then branching off to various
subtopics from there. These results illustrate the current state of consensus
formation (including variations and disagreements) among the network science
community on what should be taught about networks and how, which may also be
informative for K--12 education and informal education.Comment: 17 pages, 11 figures, 2 tables; to appear in Cramer, C. et al.
(eds.), Network Science in Education -- Tools and Techniques for Transforming
Teaching and Learning (Springer, 2017, in press
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