Indiana University-Purdue University Indianapolis (IUPUI) / Temporal point processes (TPP) are mathematical approaches for modeling asynchronous
event sequences by considering the temporal dependency of each event on past events and its
instantaneous rate. Temporal point processes can model various problems, from earthquake
aftershocks, trade orders, gang violence, and reported crime patterns, to network analysis,
infectious disease transmissions, and virus spread forecasting. In each of these cases, the
entity’s behavior with the corresponding information is noted over time as an asynchronous
event sequence, and the analysis is done using temporal point processes, which provides a
means to define the generative mechanism of the sequence of events and ultimately predict
events and investigate causality.
Among point processes, Hawkes process as a stochastic point process is able to model
a wide range of contagious and self-exciting patterns. One of Hawkes process’s well-known
applications is predicting the evolution of viral processes on networks, which is an important
problem in biology, the social sciences, and the study of the Internet. In existing works,
mean-field analysis based upon degree distribution is used to predict viral spreading across
networks of different types. However, it has been shown that degree distribution alone
fails to predict the behavior of viruses on some real-world networks. Recent attempts have
been made to use assortativity to address this shortcoming. This thesis illustrates how the
evolution of such a viral process is sensitive to the underlying network’s structure.
In Chapter 3 , we show that adding assortativity does not fully explain the variance in
the spread of viruses for a number of real-world networks. We propose using the graphlet
frequency distribution combined with assortativity to explain variations in the evolution
of viral processes across networks with identical degree distribution. Using a data-driven
approach, by coupling predictive modeling with viral process simulation on real-world networks,
we show that simple regression models based on graphlet frequency distribution can
explain over 95% of the variance in virality on networks with the same degree distribution
but different network topologies. Our results highlight the importance of graphlets and identify
a small collection of graphlets that may have the most significant influence over the viral
processes on a network.
Due to the flexibility and expressiveness of deep learning techniques, several neural
network-based approaches have recently shown promise for modeling point process intensities.
However, there is a lack of research on the possible adversarial attacks and the
robustness of such models regarding adversarial attacks and natural shocks to systems.
Furthermore, while neural point processes may outperform simpler parametric models on
in-sample tests, how these models perform when encountering adversarial examples or sharp
non-stationary trends remains unknown.
In Chapter 4 , we propose several white-box and black-box adversarial attacks against
deep temporal point processes. Additionally, we investigate the transferability of whitebox
adversarial attacks against point processes modeled by deep neural networks, which are
considered a more elevated risk. Extensive experiments confirm that neural point processes
are vulnerable to adversarial attacks. Such a vulnerability is illustrated both in terms of
predictive metrics and the effect of attacks on the underlying point process’s parameters.
Expressly, adversarial attacks successfully transform the temporal Hawkes process regime
from sub-critical to into a super-critical and manipulate the modeled parameters that is
considered a risk against parametric modeling approaches. Additionally, we evaluate the
vulnerability and performance of these models in the presence of non-stationary abrupt
changes, using the crimes and Covid-19 pandemic dataset as an example.
Considering the security vulnerability of deep-learning models, including deep temporal
point processes, to adversarial attacks, it is essential to ensure the robustness of the deployed
algorithms that is despite the success of deep learning techniques in modeling temporal point
processes.
In Chapter 5 , we study the robustness of deep temporal point processes against several
proposed adversarial attacks from the adversarial defense viewpoint. Specifically, we
investigate the effectiveness of adversarial training using universal adversarial samples in
improving the robustness of the deep point processes. Additionally, we propose a general
point process domain-adopted (GPDA) regularization, which is strictly applicable to temporal
point processes, to reduce the effect of adversarial attacks and acquire an empirically
robust model. In this approach, unlike other computationally expensive approaches, there
is no need for additional back-propagation in the training step, and no further network isrequired. Ultimately, we propose an adversarial detection framework that has been trained
in the Generative Adversarial Network (GAN) manner and solely on clean training data.
Finally, in Chapter 6 , we discuss implications of the research and future research directions.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/29984 |
| Date | 08 1900 |
| Creators | Khorshidi, Samira |
| Contributors | Mohler, George, Al Hasan, Mohammad, Raje, Rajeev, Durresi, Arjan |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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