51,678 research outputs found
Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis
We lay the foundation for a benchmarking methodology for assessing current
and future quantum computers. We pose and begin addressing fundamental
questions about how to fairly compare computational devices at vastly different
stages of technological maturity. We critically evaluate and offer our own
contributions to current quantum benchmarking efforts, in particular those
involving adiabatic quantum computation and the Adiabatic Quantum Optimizers
produced by D-Wave Systems, Inc. We find that the performance of D-Wave's
Adiabatic Quantum Optimizers scales roughly on par with classical approaches
for some hard combinatorial optimization problems; however, architectural
limitations of D-Wave devices present a significant hurdle in evaluating
real-world applications. In addition to identifying and isolating such
limitations, we develop algorithmic tools for circumventing these limitations
on future D-Wave devices, assuming they continue to grow and mature at an
exponential rate for the next several years.Comment: 117 pages. Originally published June, 201
Detecting cyber threats through social network analysis: short survey
This article considers a short survey of basic methods of social networks
analysis, which are used for detecting cyber threats. The main types of social
network threats are presented. Basic methods of graph theory and data mining,
that deals with social networks analysis are described. Typical security tasks
of social network analysis, such as community detection in network, detection
of leaders in communities, detection experts in networks, clustering text
information and others are considered
MODULAR: Software for the Autonomous Computation of Modularity in Large Network Sets
Ecological systems can be seen as networks of interactions between
individual, species, or habitat patches. A key feature of many ecological
networks is their organization into modules, which are subsets of elements that
are more connected to each other than to the other elements in the network. We
introduce MODULAR to perform rapid and autonomous calculation of modularity in
sets of networks. MODULAR reads a set of files with matrices or edge lists that
represent unipartite or bipartite networks, and identify modules using two
different modularity metrics that have been previously used in studies of
ecological networks. To find the network partition that maximizes modularity,
the software offers five optimization methods to the user. We also included two
of the most common null models that are used in studies of ecological networks
to verify how the modularity found by the maximization of each metric differs
from a theoretical benchmark
DynComm R Package -- Dynamic Community Detection for Evolving Networks
Nowadays, the analysis of dynamics in networks represents a great deal in the
Social Network Analysis research area. To support students, teachers,
developers, and researchers in this work we introduce a novel R package, namely
DynComm. It is designed to be a multi-language package, that can be used for
community detection and analysis on dynamic networks. The package introduces
interfaces to facilitate further developments and the addition of new and
future developed algorithms to deal with community detection in evolving
networks. This new package has the goal of abstracting the programmatic
interface of the algorithms, whether they are written in R or other languages,
and expose them as functions in R
Beyond News Contents: The Role of Social Context for Fake News Detection
Social media is becoming popular for news consumption due to its fast
dissemination, easy access, and low cost. However, it also enables the wide
propagation of fake news, i.e., news with intentionally false information.
Detecting fake news is an important task, which not only ensures users to
receive authentic information but also help maintain a trustworthy news
ecosystem. The majority of existing detection algorithms focus on finding clues
from news contents, which are generally not effective because fake news is
often intentionally written to mislead users by mimicking true news. Therefore,
we need to explore auxiliary information to improve detection. The social
context during news dissemination process on social media forms the inherent
tri-relationship, the relationship among publishers, news pieces, and users,
which has potential to improve fake news detection. For example,
partisan-biased publishers are more likely to publish fake news, and
low-credible users are more likely to share fake news. In this paper, we study
the novel problem of exploiting social context for fake news detection. We
propose a tri-relationship embedding framework TriFN, which models
publisher-news relations and user-news interactions simultaneously for fake
news classification. We conduct experiments on two real-world datasets, which
demonstrate that the proposed approach significantly outperforms other baseline
methods for fake news detection.Comment: In Proceedings of 12th ACM International Conference on Web Search and
Data Mining (WSDM 2019
Improving Community Detection by Mining Social Interactions
Social relationships can be divided into different classes based on the
regularity with which they occur and the similarity among them. Thus, rare and
somewhat similar relationships are random and cause noise in a social network,
thus hiding the actual structure of the network and preventing an accurate
analysis of it. In this context, in this paper we propose a process to handle
social network data that exploits temporal features to improve the detection of
communities by existing algorithms. By removing random interactions, we observe
that social networks converge to a topology with more purely social
relationships and more modular communities
Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models
We present a novel, real-time algorithm to track the trajectory of each
pedestrian in moderately dense crowded scenes. Our formulation is based on an
adaptive particle-filtering scheme that uses a combination of various
multi-agent heterogeneous pedestrian simulation models. We automatically
compute the optimal parameters for each of these different models based on
prior tracked data and use the best model as motion prior for our
particle-filter based tracking algorithm. We also use our "mixture of motion
models" for adaptive particle selection and accelerate the performance of the
online tracking algorithm. The motion model parameter estimation is formulated
as an optimization problem, and we use an approach that solves this
combinatorial optimization problem in a model independent manner and hence
scalable to any multi-agent pedestrian motion model. We evaluate the
performance of our approach on different crowd video datasets and highlight the
improvement in accuracy over homogeneous motion models and a baseline
mean-shift based tracker. In practice, our formulation can compute trajectories
of tens of pedestrians on a multi-core desktop CPU in in real time and offer
higher accuracy as compared to prior real time pedestrian tracking algorithms
Leaders, Followers, and Community Detection
Communities in social networks or graphs are sets of well-connected,
overlapping vertices. The effectiveness of a community detection algorithm is
determined by accuracy in finding the ground-truth communities and ability to
scale with the size of the data. In this work, we provide three contributions.
First, we show that a popular measure of accuracy known as the F1 score, which
is between 0 and 1, with 1 being perfect detection, has an information lower
bound is 0.5. We provide a trivial algorithm that produces communities with an
F1 score of 0.5 for any graph! Somewhat surprisingly, we find that popular
algorithms such as modularity optimization, BigClam and CESNA have F1 scores
less than 0.5 for the popular IMDB graph. To rectify this, as the second
contribution we propose a generative model for community formation, the
sequential community graph, which is motivated by the formation of social
networks. Third, motivated by our generative model, we propose the
leader-follower algorithm (LFA). We prove that it recovers all communities for
sequential community graphs by establishing a structural result that sequential
community graphs are chordal. For a large number of popular social networks, it
recovers communities with a much higher F1 score than other popular algorithms.
For the IMDB graph, it obtains an F1 score of 0.81. We also propose a
modification to the LFA called the fast leader-follower algorithm (FLFA) which
in addition to being highly accurate, is also fast, with a scaling that is
almost linear in the network size.Comment: 11 pages, 6 figure
Uncovering the Social Interaction in Swarm Intelligence with Network Science
Swarm intelligence is the collective behavior emerging in systems with
locally interacting components. Because of their self-organization
capabilities, swarm-based systems show essential properties for handling
real-world problems such as robustness, scalability, and flexibility. Yet, we
do not know why swarm-based algorithms work well and neither we can compare the
different approaches in the literature. The lack of a common framework capable
of characterizing these several swarm-based algorithms, transcending their
particularities, has led to a stream of publications inspired by different
aspects of nature without a systematic comparison over existing approaches.
Here, we address this gap by introducing a network-based framework---the
interaction network---to examine computational swarm-based systems via the
optics of the social dynamics of such interaction network; a clear example of
network science being applied to bring further clarity to a complicated field
within artificial intelligence. We discuss the social interactions of four
well-known swarm-based algorithms and provide an in-depth case study of the
Particle Swarm Optimization. The interaction network enables researchers to
study swarm algorithms as systems, removing the algorithm particularities from
the analyses while focusing on the structure of the social interactions.Comment: 23 pages, 6 figure
- …