1,405 research outputs found
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Polyphonic Sound Event Detection by using Capsule Neural Networks
Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms
Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS
The features of collaboration patterns are often considered to be different
from discipline to discipline. Meanwhile, collaborating among disciplines is an
obvious feature emerged in modern scientific research, which incubates several
interdisciplines. The features of collaborations in and among the disciplines
of biological, physical and social sciences are analyzed based on 52,803 papers
published in a multidisciplinary journal PNAS during 1999 to 2013. From those
data, we found similar transitivity and assortativity of collaboration patterns
as well as the identical distribution type of collaborators per author and that
of papers per author, namely a mixture of generalized Poisson and power-law
distributions. In addition, we found that interdisciplinary research is
undertaken by a considerable fraction of authors, not just those with many
collaborators or those with many papers. This case study provides a window for
understanding aspects of multidisciplinary and interdisciplinary collaboration
patterns
The Effect of Gender in the Publication Patterns in Mathematics
Despite the increasing number of women graduating in mathematics, a systemic
gender imbalance persists and is signified by a pronounced gender gap in the
distribution of active researchers and professors. Especially at the level of
university faculty, women mathematicians continue being drastically
underrepresented, decades after the first affirmative action measures have been
put into place. A solid publication record is of paramount importance for
securing permanent positions. Thus, the question arises whether the publication
patterns of men and women mathematicians differ in a significant way. Making
use of the zbMATH database, one of the most comprehensive metadata sources on
mathematical publications, we analyze the scholarly output of ~150,000
mathematicians from the past four decades whose gender we algorithmically
inferred. We focus on development over time, collaboration through
coautorships, presumed journal quality and distribution of research topics --
factors known to have a strong impact on job perspectives. We report
significant differences between genders which may put women at a disadvantage
when pursuing an academic career in mathematics.Comment: 24 pages, 12 figure
Contrastive Meta-Learning for Few-shot Node Classification
Few-shot node classification, which aims to predict labels for nodes on
graphs with only limited labeled nodes as references, is of great significance
in real-world graph mining tasks. Particularly, in this paper, we refer to the
task of classifying nodes in classes with a few labeled nodes as the few-shot
node classification problem. To tackle such a label shortage issue, existing
works generally leverage the meta-learning framework, which utilizes a number
of episodes to extract transferable knowledge from classes with abundant
labeled nodes and generalizes the knowledge to other classes with limited
labeled nodes. In essence, the primary aim of few-shot node classification is
to learn node embeddings that are generalizable across different classes. To
accomplish this, the GNN encoder must be able to distinguish node embeddings
between different classes, while also aligning embeddings for nodes in the same
class. Thus, in this work, we propose to consider both the intra-class and
inter-class generalizability of the model. We create a novel contrastive
meta-learning framework on graphs, named COSMIC, with two key designs. First,
we propose to enhance the intra-class generalizability by involving a
contrastive two-step optimization in each episode to explicitly align node
embeddings in the same classes. Second, we strengthen the inter-class
generalizability by generating hard node classes via a novel
similarity-sensitive mix-up strategy. Extensive experiments on few-shot node
classification datasets verify the superiority of our framework over
state-of-the-art baselines. Our code is provided at
https://github.com/SongW-SW/COSMIC.Comment: SIGKDD 202
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