275 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto}
standard in graph representation learning. However, when it comes to link
prediction, they often struggle, surpassed by simple heuristics such as Common
Neighbor (CN). This discrepancy stems from a fundamental limitation: while
MPNNs excel in node-level representation, they stumble with encoding the joint
structural features essential to link prediction, like CN. To bridge this gap,
we posit that, by harnessing the orthogonality of input vectors, pure
message-passing can indeed capture joint structural features. Specifically, we
study the proficiency of MPNNs in approximating CN heuristics. Based on our
findings, we introduce the Message Passing Link Predictor (MPLP), a novel link
prediction model. MPLP taps into quasi-orthogonal vectors to estimate
link-level structural features, all while preserving the node-level
complexities. Moreover, our approach demonstrates that leveraging
message-passing to capture structural features could offset MPNNs'
expressiveness limitations at the expense of estimation variance. We conduct
experiments on benchmark datasets from various domains, where our method
consistently outperforms the baseline methods.Comment: preprin
Geometric Learning on Graph Structured Data
Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs.
This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution.
In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Adversarial attacks and defenses in machine learning and deep neural network
have been gaining significant attention due to the rapidly growing applications
of deep learning in the Internet and relevant scenarios. This survey provides a
comprehensive overview of the recent advancements in the field of adversarial
attack and defense techniques, with a focus on deep neural network-based
classification models. Specifically, we conduct a comprehensive classification
of recent adversarial attack methods and state-of-the-art adversarial defense
techniques based on attack principles, and present them in visually appealing
tables and tree diagrams. This is based on a rigorous evaluation of the
existing works, including an analysis of their strengths and limitations. We
also categorize the methods into counter-attack detection and robustness
enhancement, with a specific focus on regularization-based methods for
enhancing robustness. New avenues of attack are also explored, including
search-based, decision-based, drop-based, and physical-world attacks, and a
hierarchical classification of the latest defense methods is provided,
highlighting the challenges of balancing training costs with performance,
maintaining clean accuracy, overcoming the effect of gradient masking, and
ensuring method transferability. At last, the lessons learned and open
challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure
Torsion Graph Neural Networks
Geometric deep learning (GDL) models have demonstrated a great potential for
the analysis of non-Euclidian data. They are developed to incorporate the
geometric and topological information of non-Euclidian data into the end-to-end
deep learning architectures. Motivated by the recent success of discrete Ricci
curvature in graph neural network (GNNs), we propose TorGNN, an analytic
Torsion enhanced Graph Neural Network model. The essential idea is to
characterize graph local structures with an analytic torsion based weight
formula. Mathematically, analytic torsion is a topological invariant that can
distinguish spaces which are homotopy equivalent but not homeomorphic. In our
TorGNN, for each edge, a corresponding local simplicial complex is identified,
then the analytic torsion (for this local simplicial complex) is calculated,
and further used as a weight (for this edge) in message-passing process. Our
TorGNN model is validated on link prediction tasks from sixteen different types
of networks and node classification tasks from three types of networks. It has
been found that our TorGNN can achieve superior performance on both tasks, and
outperform various state-of-the-art models. This demonstrates that analytic
torsion is a highly efficient topological invariant in the characterization of
graph structures and can significantly boost the performance of GNNs
Graph Neural Networks for Link Prediction with Subgraph Sketching
Many Graph Neural Networks (GNNs) perform poorly compared to simple
heuristics on Link Prediction (LP) tasks. This is due to limitations in
expressive power such as the inability to count triangles (the backbone of most
LP heuristics) and because they can not distinguish automorphic nodes (those
having identical structural roles). Both expressiveness issues can be
alleviated by learning link (rather than node) representations and
incorporating structural features such as triangle counts. Since explicit link
representations are often prohibitively expensive, recent works resorted to
subgraph-based methods, which have achieved state-of-the-art performance for
LP, but suffer from poor efficiency due to high levels of redundancy between
subgraphs. We analyze the components of subgraph GNN (SGNN) methods for link
prediction. Based on our analysis, we propose a novel full-graph GNN called
ELPH (Efficient Link Prediction with Hashing) that passes subgraph sketches as
messages to approximate the key components of SGNNs without explicit subgraph
construction. ELPH is provably more expressive than Message Passing GNNs
(MPNNs). It outperforms existing SGNN models on many standard LP benchmarks
while being orders of magnitude faster. However, it shares the common GNN
limitation that it is only efficient when the dataset fits in GPU memory.
Accordingly, we develop a highly scalable model, called BUDDY, which uses
feature precomputation to circumvent this limitation without sacrificing
predictive performance. Our experiments show that BUDDY also outperforms SGNNs
on standard LP benchmarks while being highly scalable and faster than ELPH.Comment: 29 pages, 19 figures, 6 appendice
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