12,557 research outputs found
Understanding Graph Data Through Deep Learning Lens
Deep neural network models have established themselves as an unparalleled force in the domains
of vision, speech and text processing applications in recent years. However, graphs have formed a
significant component of data analytics including applications in Internet of Things, social networks,
pharmaceuticals and bioinformatics. An important characteristic of these deep learning techniques
is their ability to learn the important features which are necessary to excel at a given task, unlike
traditional machine learning algorithms which are dependent on handcrafted features. However,
there have been comparatively fewer e�orts in deep learning to directly work on graph inputs.
Various real-world problems can be easily solved by posing them as a graph analysis problem.
Considering the direct impact of the success of graph analysis on business outcomes, importance of
studying these complex graph data has increased exponentially over the years.
In this thesis, we address three contributions towards understanding graph data: (i) The first
contribution seeks to find anomalies in graphs using graphical models; (ii) The second contribution
uses deep learning with spatio-temporal random walks to learn representations of graph trajectories
(paths) and shows great promise on standard graph datasets; and (iii) The third contribution seeks
to propose a novel deep neural network that implicitly models attention to allow for interpretation
of graph classification.
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
Time series prediction is an important problem in machine learning. Previous
methods for time series prediction did not involve additional information. With
a lot of dynamic knowledge graphs available, we can use this additional
information to predict the time series better. Recently, there has been a focus
on the application of deep representation learning on dynamic graphs. These
methods predict the structure of the graph by reasoning over the interactions
in the graph at previous time steps. In this paper, we propose a new framework
to incorporate the information from dynamic knowledge graphs for time series
prediction. We show that if the information contained in the graph and the time
series data are closely related, then this inter-dependence can be used to
predict the time series with improved accuracy. Our framework, DArtNet, learns
a static embedding for every node in the graph as well as a dynamic embedding
which is dependent on the dynamic attribute value (time-series). Then it
captures the information from the neighborhood by taking a relation specific
mean and encodes the history information using RNN. We jointly train the model
link prediction and attribute prediction. We evaluate our method on five
specially curated datasets for this problem and show a consistent improvement
in time series prediction results. We release the data and code of model
DArtNet for future research at https://github.com/INK-USC/DArtNet .Comment: In Proceedings of IJCAI 2020. Code can be found at
https://github.com/INK-USC/DArtNet . The sole copyright holder is IJCAI
(International Joint Conferences on Artificial Intelligence), all rights
reserved. Original Publication available at
https://www.ijcai.org/Proceedings/2020/38
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to its
wide range of applications, generative models for graphs have a rich history,
which, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for the graph generation. Firstly, the formal definition of
deep generative models for the graph generation as well as preliminary
knowledge is provided. Secondly, two taxonomies of deep generative models for
unconditional, and conditional graph generation respectively are proposed; the
existing works of each are compared and analyzed. After that, an overview of
the evaluation metrics in this specific domain is provided. Finally, the
applications that deep graph generation enables are summarized and five
promising future research directions are highlighted
A Survey on Malware Detection with Graph Representation Learning
Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and
heuristics are used for malware detection, but unfortunately, they suffer from
poor generalization to unknown attacks and can be easily circumvented using
obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep
Learning (DL) achieved impressive results in malware detection by learning
useful representations from data and have become a solution preferred over
traditional methods. More recently, the application of such techniques on
graph-structured data has achieved state-of-the-art performance in various
domains and demonstrates promising results in learning more robust
representations from malware. Yet, no literature review focusing on graph-based
deep learning for malware detection exists. In this survey, we provide an
in-depth literature review to summarize and unify existing works under the
common approaches and architectures. We notably demonstrate that Graph Neural
Networks (GNNs) reach competitive results in learning robust embeddings from
malware represented as expressive graph structures, leading to an efficient
detection by downstream classifiers. This paper also reviews adversarial
attacks that are utilized to fool graph-based detection methods. Challenges and
future research directions are discussed at the end of the paper.Comment: Preprint, submitted to ACM Computing Surveys on March 2023. For any
suggestions or improvements, please contact me directly by e-mai
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
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