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16th International Conference on Web Search and Data Mining

作者： Derr, Tyler Ma, Yao Rozemberczki, Benedek Shah, Neil Pan, Shirui Vanderbilt Univ Nashville TN 37235 USA New Jersey Inst Technol Newark NJ USA Isomorph Labs London England Snap Inc Santa Monica CA USA Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450394079

graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd International Workshop on machine learning on graphs (MLoG)(1), held in conjunction with the 16th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on graphs

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The 5th InternationalWorkshop on machine learning on graphs (MLoG) 24

The 5th InternationalWorkshop on Machine Learning on Graphs ...

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17th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Derr, Tyler Ma, Yao Ding, Kaize Zhao, Tong Ahmed, Nesreen K. Vanderbilt Univ Nashville TN 37235 USA Rensselaer Polytech Inst Troy NY USA Northwestern Univ Evanston IL USA Snap Inc Santa Monica CA USA Intel Labs Hillsboro OR USA

ISBN: (纸本)9798400703713

graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 5th International Workshop on machine learning on graphs (MLoG)(1), held in conjunction with the 17th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.

关键词： machine learning on graphs

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Data Analytics on graphs Part III: machine learning on graphs, from Graph Topology to Applications

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FOUNDATIONS AND TRENDS IN machine learning 2020年第4期13卷 332-530页

作者： Stankovic, Ljubisa Mandic, Danilo Dakovic, Milos Brajovic, Milos Scalzo, Bruno Li, Shengxi Constantinides, Anthony G. Univ Montenegro Podgorica Montenegro Imperial Coll London London England

Modern data analytics applications on graphs often operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution. Part III of this monograph starts by a comprehensive account of ways to learn the pertinent graph topology, ranging from the simplest case where the physics of the problem already suggest a possible graph structure, through to general cases where the graph structure is to be learned from the data observed on a graph. A particular emphasis is placed on the use of standard "relationship measures" in this context, including the correlation and precision matrices, together with the ways to combine these with the available prior knowledge and structural conditions, such as the smoothness of the graph signals or sparsity of graph connections. Next, for learning sparse graphs (that is, graphs with a small number of edges), the utility of the least absolute shrinkage and selection operator, known as (LASSO) is addressed, along with its graph specific variant, the graphical LASSO. For completeness, both variants of LASSO are derived in an intuitive way, starting from basic principles. An in-depth elaboration of the graph topology learning paradigm is provided through examples on physically well defined graphs, such as electric circuits, linear heat transfer, social and computer networks, and spring-mass systems. We also review main trends in graph neural networks (GNN) and graph convolutional networks (GCN) from the perspective of graph signal filtering. Particular insight is given to the role of diffusion processes over graphs, to show that GCNs can be understood from the graph diffusion perspective. Given the largely heuristic nature of the existing GCNs, their treatment through graph diffusion processes may also serve as a basis for new designs of GCNs. Tensor representation of lattice-structured graphs is next considered,

关键词： graph theory random data on graphs big data on graphs signal processing on graphs machine learning on graphs graph topology learning systems on graphs vertex-frequency estimation graph neural networks graphs and tensors

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Empowering Simple Graph Convolutional Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第4期35卷 4385-4399页

作者： Pasa, Luca Navarin, Nicolo Erb, Wolfgang Sperduti, Alessandro Univ Padua Dept Math Tullio Levi Civita I-35121 Padua Italy Univ Trento DISI I-38123 Trento Italy

Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, which tend to add complexity (and nonlinearity) to the model. Recently, however, a simplified GC operator, dubbed simple graph convolution (SGC), which aims to remove nonlinearities was proposed. Motivated by the good results reached by this simpler model, in this article we propose, analyze, and compare simple graph convolution operators of increasing complexity that rely on linear transformations or controlled nonlinearities, and that can be implemented in single-layer graph convolutional networks (GCNs). Their computational expressiveness is characterized as well. We show that the predictive performance of the proposed GC operators is competitive with the ones of other widely adopted models on the considered node classification benchmark datasets.

关键词： Deep learning graph convolution (GC) graph neural network (GNN) machine learning on graphs structured data

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Multiresolution Reservoir Graph Neural Network

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2022年第6期33卷 2642-2653页

作者： Pasa, Luca Navarin, Nicolo Sperduti, Alessandro Univ Padua Human Inspired Technol Res Ctr HIT Dept Math Tullio Levi Civita I-35121 Padua Italy

Graph neural networks are receiving increasing attention as state-of-the-art methods to process graph-structured data. However, similar to other neural networks, they tend to suffer from a high computational cost to perform training. Reservoir computing (RC) is an effective way to define neural networks that are very efficient to train, often obtaining comparable predictive performance with respect to the fully trained counterparts. Different proposals of reservoir graph neural networks have been proposed in the literature. However, their predictive performances are still slightly below the ones of fully trained graph neural networks on many benchmark datasets, arguably because of the oversmoothing problem that arises when iterating over the graph structure in the reservoir computation. In this work, we aim to reduce this gap defining a multiresolution reservoir graph neural network (MRGNN) inspired by graph spectral filtering. Instead of iterating on the nonlinearity in the reservoir and using a shallow readout function, we aim to generate an explicit k-hop unsupervised graph representation amenable for further, possibly nonlinear, processing. Experiments on several datasets from various application areas show that our approach is extremely fast and it achieves in most of the cases comparable or even higher results with respect to state-of-the-art approaches.

关键词： Convolution Reservoirs Training Computational modeling Neural networks Graph neural networks Standards Deep learning graph neural network machine learning on graphs reservoir computing (RC) structured data

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A unified framework for backpropagation-free soft and hard gated graph neural networks

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第4期66卷 2393-2416页

作者： Pasa, Luca Navarin, Nicolo Erb, Wolfgang Sperduti, Alessandro Univ Padua Dept Math Padua Italy Univ Trento DISI Trento Italy

We propose a framework for the definition of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation. Our proposed framework is inspired by Gated Linear Networks and allows the adoption of multiple graph convolutions. Specifically, each neuron is defined as a set of graph convolution filters (weight vectors) and a gating mechanism that, given a node and its topological context, generates the weight vector to use for processing the node's attributes. Two different graph processing schemes are studied, i.e., a message-passing aggregation scheme where the gating mechanism is embedded directly into the graph convolution, and a multi-resolution one where neighboring nodes at different topological distances are jointly processed by a single graph convolution layer. We also compare the effectiveness of different alternatives for defining the context function of a node, i.e., based on hyperplanes or on prototypes, and using a soft or hard-gating mechanism. We propose a unified theoretical framework allowing us to theoretically characterize the proposed models' expressiveness. We experimentally evaluate our backpropagation-free graph convolutional neural models on commonly adopted node classification datasets and show competitive performances compared to the backpropagation-based counterparts.

关键词： Graph convolutional networks Graph neural network Deep learning Structured data machine learning on graphs

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Polynomial-based graph convolutional neural networks for graph classification

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machine learning 2022年第4期111卷 1205-1237页

作者： Pasa, Luca Navarin, Nicolo Sperduti, Alessandro Univ Padua Dept Math Padua Italy Univ Padua Human Inspired Technol Res Ctr Padua Italy

Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of graphs. The most common convolution operators only exploit local topological information. To consider wider topological receptive fields, the mainstream approach is to non-linearly stack multiple graph convolutional (GC) layers. In this way, however, interactions among GC parameters at different levels pose a bias on the flow of topological information. In this paper, we propose a different strategy, considering a single graph convolution layer that independently exploits neighbouring nodes at different topological distances, generating decoupled representations for each of them. These representations are then processed by subsequent readout layers. We implement this strategy introducing the polynomial graph convolution (PGC) layer, that we prove being more expressive than the most common convolution operators and their linear stacking. Our contribution is not limited to the definition of a convolution operator with a larger receptive field, but we prove both theoretically and experimentally that the common way multiple non-linear graph convolutions are stacked limits the neural network expressiveness. Specifically, we show that a graph neural network architecture with a single PGC layer achieves state of the art performance on many commonly adopted graph classification benchmarks.

关键词： Graph convolutional networks Graph neural network Deep learning Structured data machine learning on graphs

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Scalable Semi-Supervised Graph learning Techniques for Anti Money Laundering

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IEEE ACCESS 2024年 12卷 50012-50029页

作者： Karim, Md. Rezaul Hermsen, Felix Chala, Sisay Adugna De Perthuis, Paola Mandal, Avikarsha Rhein Westfal TH Aachen Dept Informat Syst & Databases D-52074 Aachen Germany Fraunhofer FIT Dept Data Sci & Artificial Intelligence D-53757 St Augustin Germany Ecole Normale Super ENS F-75005 Paris France Cosmian F-75008 Paris France

Money laundering is the process by which criminals move large sums of illicit money to hidden locations and integrate them as legal funds through existing financial services. The United Nations (UN) estimates that 2 to 5% of global GDP, which is approximately 0.8 to 2.0 trillion dollars, is laundered globally every year. Therefore, accurately identifying such globally alarming activities is crucial for enforcing anti-money laundering (AML) measures. Numerous techniques have been proposed to detect money laundering from transaction graphs of money transfers between bank accounts by analysing the structural and behavioural dynamics of their corresponding dense subgraphs. However, these techniques often do not consider that money laundering usually involves high-volume flows of funds through chains of bank accounts. Moreover, most AML approaches either result in lower detection accuracy or incur higher computational costs, making them less reliable and suitable for real financial systems. Consequently, only a fraction of money laundering activities can be detected and prevented. In this paper, we propose an efficient approach to AML by employing semi-supervised graph learning techniques on a large-scale financial transactional graph in both pipeline settings (i.e., graph embedding models are first trained to generate node embeddings that are combined with additional topological graph features to train binary classifiers) and end-to-end settings (i.e., node classification is performed by training SkipGCN, FastGCN, and EvolveGCN without requiring separate classifiers) to identify nodes involved in potential money laundering activities. We evaluate our approach on four datasets: AMLSim, Elliptic, IBM AML, and SynthAML, with a view to scalability and practicality for real financial systems. Further, we provide local (e.g., how money is laundered between nodes) and global (e.g., what factors contribute to money laundering) explanations of the predictions by highlighting the

关键词： Topology Reliability Fraud Data models Vectors Synthetic data Semisupervised learning Anti-money laundering machine learning on graphs graph embedding

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Fair Contrastive learning on graphs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2022年 8卷 475-488页

作者： Kose, Oyku Deniz Shen, Yanning Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92623 USA

Node representation learning plays a critical role in learning over graphs. Specifically, the success of contrastive learning methods in unsupervised node representation learning has been demonstrated for various tasks, which has led to increase in attention towards the field. Despite the increasing popularity, fairness is widely under-explored in the area. Motivated by this, this study proposes novel fairness-aware graph augmentations based on adaptive feature masking and edge deletion, in order to mitigate the bias in graph contrastive learning. Different fairness notions on graphs are introduced in the study to guide the designs of the proposed adaptive augmentation schemes. Moreover, it is quantitatively shown that the proposed feature masking scheme can reduce the intrinsic bias. Experimental results on four real-world networks are presented to show that the introduced augmentation frameworks can improve group fairness measures together with comparable classification accuracy to state-of-the-art graph contrastive learning studies for node classification.

关键词： machine learning on graphs fairness graph contrastive learning data augmentation node representation learning

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A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring

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APPLIED NETWORK SCIENCE 2021年第1期6卷 1-24页

作者： Bloemheuvel, Stefan van den Hoogen, Jurgen Atzmueller, Martin Tilburg Univ Tilburg Netherlands Jheronimus Acad Data Sci SHertogenbosch Netherlands Osnabruck Univ Semant Informat Syst Grp Osnabruck Germany

Complex networks lend themselves for the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options. In this paper, we target this-connected to specific machine learning approaches on graphs for Structural Health Monitoring (SHM) from an analysis and predictive (maintenance) perspective. Specifically, we present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We demonstrate this framework in our targeted application domain of SHM. In particular, we focus on a prominent real-world SHM use case, i. e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands. In our experiments, we show that GSP enables the identification of the most important sensors, for which we investigate a set of search and optimization approaches. Furthermore, GSP enables the detection of specific graph signal patterns (i. e., mode shapes), capturing physical functional properties of the sensors in the applied complex network. In addition, we show the efficacy of applying GNNs for strain prediction utilizing this kind of sensor data.

关键词： Complex networks Graph signal processing Sensor data Complex networks for physical infrastructures Structural health monitoring Graph neural networks machine learning on graphs

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