29,227 research outputs found
Persistent Homology Guided Force-Directed Graph Layouts
Graphs are commonly used to encode relationships among entities, yet their
abstractness makes them difficult to analyze. Node-link diagrams are popular
for drawing graphs, and force-directed layouts provide a flexible method for
node arrangements that use local relationships in an attempt to reveal the
global shape of the graph. However, clutter and overlap of unrelated structures
can lead to confusing graph visualizations. This paper leverages the persistent
homology features of an undirected graph as derived information for interactive
manipulation of force-directed layouts. We first discuss how to efficiently
extract 0-dimensional persistent homology features from both weighted and
unweighted undirected graphs. We then introduce the interactive persistence
barcode used to manipulate the force-directed graph layout. In particular, the
user adds and removes contracting and repulsing forces generated by the
persistent homology features, eventually selecting the set of persistent
homology features that most improve the layout. Finally, we demonstrate the
utility of our approach across a variety of synthetic and real datasets
Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition
We focus on the election manipulation problem through social influence, where
a manipulator exploits a social network to make her most preferred candidate
win an election. Influence is due to information in favor of and/or against one
or multiple candidates, sent by seeds and spreading through the network
according to the independent cascade model. We provide a comprehensive study of
the election control problem, investigating two forms of manipulations: seeding
to buy influencers given a social network, and removing or adding edges in the
social network given the seeds and the information sent. In particular, we
study a wide range of cases distinguishing for the number of candidates or the
kind of information spread over the network. Our main result is positive for
democracy, and it shows that the election manipulation problem is not
affordable in the worst-case except for trivial classes of instances, even when
one accepts to approximate the margin of victory. In the case of seeding, we
also show that the manipulation is hard even if the graph is a line and that a
large class of algorithms, including most of the approaches recently adopted
for social-influence problems, fail to compute a bounded approximation even on
elementary networks, as undirected graphs with every node having a degree at
most two or directed trees. In the case of edge removal or addition, our
hardness results also apply to the basic case of social influence
maximization/minimization. In contrast, the hardness of election manipulation
holds even when the manipulator has an unlimited budget, being allowed to
remove or add an arbitrary number of edges.Comment: arXiv admin note: text overlap with arXiv:1902.0377
Controlling edge dynamics in complex networks
The interaction of distinct units in physical, social, biological and
technological systems naturally gives rise to complex network structures.
Networks have constantly been in the focus of research for the last decade,
with considerable advances in the description of their structural and dynamical
properties. However, much less effort has been devoted to studying the
controllability of the dynamics taking place on them. Here we introduce and
evaluate a dynamical process defined on the edges of a network, and demonstrate
that the controllability properties of this process significantly differ from
simple nodal dynamics. Evaluation of real-world networks indicates that most of
them are more controllable than their randomized counterparts. We also find
that transcriptional regulatory networks are particularly easy to control.
Analytic calculations show that networks with scale-free degree distributions
have better controllability properties than uncorrelated networks, and
positively correlated in- and out-degrees enhance the controllability of the
proposed dynamics.Comment: Preprint. 24 pages, 4 figures, 2 tables. Source code available at
http://github.com/ntamas/netctr
Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election
Social media has become an emerging alternative to opinion polls for public
opinion collection, while it is still posing many challenges as a passive data
source, such as structurelessness, quantifiability, and representativeness.
Social media data with geotags provide new opportunities to unveil the
geographic locations of users expressing their opinions. This paper aims to
answer two questions: 1) whether quantifiable measurement of public opinion can
be obtained from social media and 2) whether it can produce better or
complementary measures compared to opinion polls. This research proposes a
novel approach to measure the relative opinion of Twitter users towards public
issues in order to accommodate more complex opinion structures and take
advantage of the geography pertaining to the public issues. To ensure that this
new measure is technically feasible, a modeling framework is developed
including building a training dataset by adopting a state-of-the-art approach
and devising a new deep learning method called Opinion-Oriented Word Embedding.
With a case study of the tweets selected for the 2016 U.S. presidential
election, we demonstrate the predictive superiority of our relative opinion
approach and we show how it can aid visual analytics and support opinion
predictions. Although the relative opinion measure is proved to be more robust
compared to polling, our study also suggests that the former can advantageously
complement the later in opinion prediction
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
Heritage Planning: Approaches to Public Participation and Contestation in Groningen, The Netherlands
- …