46,091 research outputs found
Detecting Core-Periphery Structures by Surprise
Detecting the presence of mesoscale structures in complex networks is of
primary importance. This is especially true for financial networks, whose
structural organization deeply affects their resilience to events like default
cascades, shocks propagation, etc. Several methods have been proposed, so far,
to detect communities, i.e. groups of nodes whose connectivity is significantly
large. Communities, however do not represent the only kind of mesoscale
structures characterizing real-world networks: other examples are provided by
bow-tie structures, core-periphery structures and bipartite structures. Here we
propose a novel method to detect statistically-signifcant bimodular structures,
i.e. either bipartite or core-periphery ones. It is based on a modification of
the surprise, recently proposed for detecting communities. Our variant allows
for bimodular nodes partitions to be revealed, by letting links to be placed
either 1) within the core part and between the core and the periphery parts or
2) just between the (empty) layers of a bipartite network. From a technical
point of view, this is achieved by employing a multinomial hypergeometric
distribution instead of the traditional (binomial) hypergeometric one; as in
the latter case, this allows a p-value to be assigned to any given
(bi)partition of the nodes. To illustrate the performance of our method, we
report the results of its application to several real-world networks, including
social, economic and financial ones.Comment: 11 pages, 10 figures. Python code freely available at
https://github.com/jeroenvldj/bimodular_surpris
Comparative Evaluation of Community Detection Algorithms: A Topological Approach
Community detection is one of the most active fields in complex networks
analysis, due to its potential value in practical applications. Many works
inspired by different paradigms are devoted to the development of algorithmic
solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance
measure considering the community structure as a partition (Rand Index,
Normalized Mutual information, etc.). However, this type of comparison neglects
the topological properties of the communities. In this article, we present a
comprehensive comparative study of a representative set of community detection
methods, in which we adopt both types of evaluation. Community-oriented
topological measures are used to qualify the communities and evaluate their
deviation from the reference structure. In order to mimic real-world systems,
we use artificially generated realistic networks. It turns out there is no
equivalence between both approaches: a high performance does not necessarily
correspond to correct topological properties, and vice-versa. They can
therefore be considered as complementary, and we recommend applying both of
them in order to perform a complete and accurate assessment
Interdisciplinary and physics challenges of Network Theory
Network theory has unveiled the underlying structure of complex systems such
as the Internet or the biological networks in the cell. It has identified
universal properties of complex networks, and the interplay between their
structure and dynamics. After almost twenty years of the field, new challenges
lie ahead. These challenges concern the multilayer structure of most of the
networks, the formulation of a network geometry and topology, and the
development of a quantum theory of networks. Making progress on these aspects
of network theory can open new venues to address interdisciplinary and physics
challenges including progress on brain dynamics, new insights into quantum
technologies, and quantum gravity.Comment: (7 pages, 4 figures
Preserving Link Privacy in Social Network Based Systems
A growing body of research leverages social network based trust relationships
to improve the functionality of the system. However, these systems expose
users' trust relationships, which is considered sensitive information in
today's society, to an adversary.
In this work, we make the following contributions. First, we propose an
algorithm that perturbs the structure of a social graph in order to provide
link privacy, at the cost of slight reduction in the utility of the social
graph. Second we define general metrics for characterizing the utility and
privacy of perturbed graphs. Third, we evaluate the utility and privacy of our
proposed algorithm using real world social graphs. Finally, we demonstrate the
applicability of our perturbation algorithm on a broad range of secure systems,
including Sybil defenses and secure routing.Comment: 16 pages, 15 figure
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
Consuming news from social media is becoming increasingly popular. However,
social media also enables the widespread of fake news. Because of its
detrimental effects brought by social media, fake news detection has attracted
increasing attention. However, the performance of detecting fake news only from
news content is generally limited as fake news pieces are written to mimic true
news. In the real world, news pieces spread through propagation networks on
social media. The news propagation networks usually involve multi-levels. In
this paper, we study the challenging problem of investigating and exploiting
news hierarchical propagation network on social media for fake news detection.
In an attempt to understand the correlations between news propagation
networks and fake news, first, we build a hierarchical propagation network from
macro-level and micro-level of fake news and true news; second, we perform a
comparative analysis of the propagation network features of linguistic,
structural and temporal perspectives between fake and real news, which
demonstrates the potential of utilizing these features to detect fake news;
third, we show the effectiveness of these propagation network features for fake
news detection. We further validate the effectiveness of these features from
feature important analysis. Altogether, this work presents a data-driven view
of hierarchical propagation network and fake news and paves the way towards a
healthier online news ecosystem.Comment: 10 page
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook
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