1,942 research outputs found
Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks
Unsupervised robust nonparametric learning of hidden community properties
We consider learning of fundamental properties of communities in large noisy
networks, in the prototypical situation where the nodes or users are split into
two classes according to a binary property, e.g., according to their opinions
or preferences on a topic. For learning these properties, we propose a
nonparametric, unsupervised, and scalable graph scan procedure that is, in
addition, robust against a class of powerful adversaries. In our setup, one of
the communities can fall under the influence of a knowledgeable adversarial
leader, who knows the full network structure, has unlimited computational
resources and can completely foresee our planned actions on the network. We
prove strong consistency of our results in this setup with minimal assumptions.
In particular, the learning procedure estimates the baseline activity of normal
users asymptotically correctly with probability 1; the only assumption being
the existence of a single implicit community of asymptotically negligible
logarithmic size. We provide experiments on real and synthetic data to
illustrate the performance of our method, including examples with adversaries.Comment: Experiments with new types of adversaries adde
Realtime market microstructure analysis: online Transaction Cost Analysis
Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.Comment: 33 pages, 12 figure
Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis
The Internet of Things (IoT) integrates more than billions of intelligent
devices over the globe with the capability of communicating with other
connected devices with little to no human intervention. IoT enables data
aggregation and analysis on a large scale to improve life quality in many
domains. In particular, data collected by IoT contain a tremendous amount of
information for anomaly detection. The heterogeneous nature of IoT is both a
challenge and an opportunity for cybersecurity. Traditional approaches in
cybersecurity monitoring often require different kinds of data pre-processing
and handling for various data types, which might be problematic for datasets
that contain heterogeneous features. However, heterogeneous types of network
devices can often capture a more diverse set of signals than a single type of
device readings, which is particularly useful for anomaly detection. In this
paper, we present a comprehensive study on using ensemble machine learning
methods for enhancing IoT cybersecurity via anomaly detection. Rather than
using one single machine learning model, ensemble learning combines the
predictive power from multiple models, enhancing their predictive accuracy in
heterogeneous datasets rather than using one single machine learning model. We
propose a unified framework with ensemble learning that utilises Bayesian
hyperparameter optimisation to adapt to a network environment that contains
multiple IoT sensor readings. Experimentally, we illustrate their high
predictive power when compared to traditional methods
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
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