339,049 research outputs found
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning,
this paper proposes a novel GAN-based disentanglement framework with One-Hot
Sampling and Orthogonal Regularization (OOGAN). While previous works mostly
attempt to tackle disentanglement learning through VAE and seek to implicitly
minimize the Total Correlation (TC) objective with various sorts of
approximation methods, we show that GANs have a natural advantage in
disentangling with an alternating latent variable (noise) sampling method that
is straightforward and robust. Furthermore, we provide a brand-new perspective
on designing the structure of the generator and discriminator, demonstrating
that a minor structural change and an orthogonal regularization on model
weights entails an improved disentanglement. Instead of experimenting on simple
toy datasets, we conduct experiments on higher-resolution images and show that
OOGAN greatly pushes the boundary of unsupervised disentanglement.Comment: AAAI 202
ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment
Recruitment of appropriate people for certain positions is critical for any
companies or organizations. Manually screening to select appropriate candidates
from large amounts of resumes can be exhausted and time-consuming. However,
there is no public tool that can be directly used for automatic resume quality
assessment (RQA). This motivates us to develop a method for automatic RQA.
Since there is also no public dataset for model training and evaluation, we
build a dataset for RQA by collecting around 10K resumes, which are provided by
a private resume management company. By investigating the dataset, we identify
some factors or features that could be useful to discriminate good resumes from
bad ones, e.g., the consistency between different parts of a resume. Then a
neural-network model is designed to predict the quality of each resume, where
some text processing techniques are incorporated. To deal with the label
deficiency issue in the dataset, we propose several variants of the model by
either utilizing the pair/triplet-based loss, or introducing some
semi-supervised learning technique to make use of the abundant unlabeled data.
Both the presented baseline model and its variants are general and easy to
implement. Various popular criteria including the receiver operating
characteristic (ROC) curve, F-measure and ranking-based average precision (AP)
are adopted for model evaluation. We compare the different variants with our
baseline model. Since there is no public algorithm for RQA, we further compare
our results with those obtained from a website that can score a resume.
Experimental results in terms of different criteria demonstrate the
effectiveness of the proposed method. We foresee that our approach would
transform the way of future human resources management.Comment: ICD
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