20,180 research outputs found
Finding k-Dissimilar Paths with Minimum Collective Length
Shortest path computation is a fundamental problem in road networks. However,
in many real-world scenarios, determining solely the shortest path is not
enough. In this paper, we study the problem of finding k-Dissimilar Paths with
Minimum Collective Length (kDPwML), which aims at computing a set of paths from
a source s to a target t such that all paths are pairwise dissimilar by at
least \theta and the sum of the path lengths is minimal. We introduce an exact
algorithm for the kDPwML problem, which iterates over all possible s-t paths
while employing two pruning techniques to reduce the prohibitively expensive
computational cost. To achieve scalability, we also define the much smaller set
of the simple single-via paths, and we adapt two algorithms for kDPwML queries
to iterate over this set. Our experimental analysis on real road networks shows
that iterating over all paths is impractical, while iterating over the set of
simple single-via paths can lead to scalable solutions with only a small
trade-off in the quality of the results.Comment: Extended version of the SIGSPATIAL'18 paper under the same titl
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
The specificity and robustness of long-distance connections in weighted, interareal connectomes
Brain areas' functional repertoires are shaped by their incoming and outgoing
structural connections. In empirically measured networks, most connections are
short, reflecting spatial and energetic constraints. Nonetheless, a small
number of connections span long distances, consistent with the notion that the
functionality of these connections must outweigh their cost. While the precise
function of these long-distance connections is not known, the leading
hypothesis is that they act to reduce the topological distance between brain
areas and facilitate efficient interareal communication. However, this
hypothesis implies a non-specificity of long-distance connections that we
contend is unlikely. Instead, we propose that long-distance connections serve
to diversify brain areas' inputs and outputs, thereby promoting complex
dynamics. Through analysis of five interareal network datasets, we show that
long-distance connections play only minor roles in reducing average interareal
topological distance. In contrast, areas' long-distance and short-range
neighbors exhibit marked differences in their connectivity profiles, suggesting
that long-distance connections enhance dissimilarity between regional inputs
and outputs. Next, we show that -- in isolation -- areas' long-distance
connectivity profiles exhibit non-random levels of similarity, suggesting that
the communication pathways formed by long connections exhibit redundancies that
may serve to promote robustness. Finally, we use a linearization of
Wilson-Cowan dynamics to simulate the covariance structure of neural activity
and show that in the absence of long-distance connections, a common measure of
functional diversity decreases. Collectively, our findings suggest that
long-distance connections are necessary for supporting diverse and complex
brain dynamics.Comment: 18 pages, 8 figure
One-Shot Traffic Assignment with Forward-Looking Penalization
Traffic assignment (TA) is crucial in optimizing transportation systems and
consists in efficiently assigning routes to a collection of trips. Existing TA
algorithms often do not adequately consider real-time traffic conditions,
resulting in inefficient route assignments. This paper introduces METIS, a
cooperative, one-shot TA algorithm that combines alternative routing with edge
penalization and informed route scoring. We conduct experiments in several
cities to evaluate the performance of METIS against state-of-the-art one-shot
methods. Compared to the best baseline, METIS significantly reduces CO2
emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip
distribution considerably while still having low computational time. Our study
proposes METIS as a promising solution for optimizing TA and urban
transportation systems
Comparing Alternative Route Planning Techniques: A Comparative User Study on Melbourne, Dhaka and Copenhagen Road Networks
Many modern navigation systems and map-based services do not only provide the
fastest route from a source location s to a target location t but also provide
a few alternative routes to the users as more options to choose from.
Consequently, computing alternative paths has received significant research
attention. However, it is unclear which of the existing approaches generates
alternative routes of better quality because the quality of these alternatives
is mostly subjective. Motivated by this, in this paper, we present a user study
conducted on the road networks of Melbourne, Dhaka and Copenhagen that compares
the quality (as perceived by the users) of the alternative routes generated by
four of the most popular existing approaches including the routes provided by
Google Maps. We also present a web-based demo system that can be accessed using
any internet-enabled device and allows users to see the alternative routes
generated by the four approaches for any pair of selected source and target. We
report the average ratings received by the four approaches and our statistical
analysis shows that there is no credible evidence that the four approaches
receive different ratings on average. We also discuss the limitations of this
user study and recommend the readers to interpret these results with caution
because certain factors may have affected the participants' ratings.Comment: Extended the user study to also include the road networks of Dhaka
and Copenhagen (the previous version only had Melbourne road network
Design of Toy Proteins Capable to Rearrange Conformations in a Mechanical Fashion
We design toy protein mimicking a machine-like function of an enzyme. Using
an insight gained by the study of conformation space of compact lattice
polymers, we demonstrate the possibility of a large scale conformational
rearrangement which occurs (i) without opening a compact state, and (ii) along
a linear (one-dimensional) path. We also demonstrate the possibility to extend
sequence design method such that it yields a "collective funnel" landscape in
which the toy protein (computationally) folds into the valley with
rearrangement path at its bottom. Energies of the states along the path can be
designed to be about equal, allowing for diffusion along the path. They can
also be designed to provide for a significant bias in one certain direction.
Together with a toy ligand molecule, our "enzimatic" machine can perform the
entire cycle, including conformational relaxation in one direction upon ligand
binding and conformational relaxation in the opposite direction upon ligand
release. This model, however schematic, should be useful as a test ground for
phenomenological theories of machine-like properties of enzymes.Comment: 13 pages, 12 figure
Innovation-based Nets as Collective Actors: A Heterarchization Case Study from the Automotive Industry
Cooperation and collaboration between companies represents a key issue within the conceptual framework developed by the IMP Group. However, little attention has been paid to a phenomenon which can result from such collaboration, i.e. collective action. This involves cooperative activities undertaken by a significant number of actors sharing a common aim. This research uses the concept of issue-based net to open new avenues to understand collective action in the context of innovation activities, specifically by analyzing a case study of an innovation-based net in the automotive industry. Two main objectives are addressed in this study: Related to this discussion of different development paths of collective actors, the case study analysis focuses on how issue-based nets emerge and evolve in situations of innovation, specifically, what kind of structure and process issues characterize a heterarchization development path. Furthermore, the analysis addressed how issue-based nets change the positioning of individual member firms, a well as that of the collective actor within the overall network.Innovation, collective actor, issue-based nets, heterarchization, case study, automotive industry
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