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Data Analytics on graphs Part II: Signals on graphs

FOUNDATIONS AND TRENDS IN machine learning 2020年第2-3期13卷 158-331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

The area of Data Analytics on graphs deals with information processing of data acquired on irregular but structured graph domains. The focus of Part I of this monograph has been on both the fundamental and higher-order graph properties, graph topologies, and spectral representations of graphs. Part I also establishes rigorous frameworks for vertex clustering and graph segmentation, and illustrates the power of graphs in various data association tasks. Part II embarks on these concepts to address the algorithmic and practical issues related to data/signal processing on graphs, with the focus on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Fourier Transform (GFT), defined based on the eigendecomposition of both the adjacency matrix and the graph Laplacian. Spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev series polynomial approximation. Ideas related to the sampling of graph signals, and in particular the challenging topic of data dimensionality reduction through graph subsampling, are presented and further linked with compressive sensing. The principles of time-varying signals on graphs and basic definitions related to random graph signals are next reviewed. Localized graph signal analysis in the joint vertex-spectral domain is referred to as the vertex-frequency analysis, since it can be considered as an extension of classical time-frequency analysis to the graph serving as signal domain. Important aspects of the local gra

关键词： graph theory random data on graphs big data on graphs signal processing on graphs machine learning on graphs graph

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Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements 34

Data-driven Predictive Latency for 5G: A Theoretical and Exp...

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IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Skocaj, Marco Conserva, Francesca Grande, Nicol Sarcone Orsi, Andrea Micheli, Davide Ghinamo, Giorgio Bizzarri, Simone Verdone, Roberto Univ Bologna DEI Bologna Italy WiLab CNIT Bologna Italy TIM Maglie Italy

ISBN: (纸本)9781665464833

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in machine learning (ML), such as Bayesian learning (BL) and machine learning on graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.

关键词： Predictive Quality of Service Latency machine learning Bayesian learning machine learning on graphs 5G

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SELF-GROWING SPATIAL GRAPH NETWORK FOR CONTEXT-AWARE PEDESTRIAN TRAJECTORY PREDICTION

SELF-GROWING SPATIAL GRAPH NETWORK FOR CONTEXT-AWARE PEDESTR...

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IEEE International Conference on Image Processing (ICIP)

作者： Haddad, Sirin Lam, Siew-Kei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781665441155

Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However, existing works rely on spatial assumptions about the scene and dynamics, which entails a significant challenge to adapt the graph structure in unknown environments for an online system. In addition, there is a lack of assessment approach for the relational modeling impact on prediction performance. To fill this gap, we propose Social Trajectory Recommender-Gated Graph Recurrent Neighborhood Network (STR-GGRNN), which uses data-driven adaptive online neighborhood recommendation based on the contextual scene features and pedestrian visual cues. The neighborhood recommendation is achieved by online Nonnegative Matrix Factorization (NMF) to construct the graph adjacency matrices for predicting the pedestrians' trajectories. Experiments based on widely-used datasets show that our method outperforms the state-of-the-art. Our best performing model achieves 12 cm ADE and similar to 15 cm FDE on ETH-UCY dataset.

关键词： machine learning on graphs Tracking Vision and Scene Understanding Nonnegative Matrix Factorization

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Simple Multi-resolution Gated GNN

Simple Multi-resolution Gated GNN

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Pasa, Luca Navarin, NicolO Sperduti, Alessandro Univ Padua Dept Math Padua Italy

ISBN: (纸本)9781728190488

Most Graph Neural Networks (GNNs) proposed in literature tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a simple linear multi-resolution architecture that implements a multi-head gating mechanism. We assessed the performances of the proposed architecture on node classification tasks. To perform a fair comparison and present significant results, we re-implemented the competing methods from the literature and ran the experimental evaluation considering two different experimental settings with different model selection procedures. The proposed convolution, dubbed Simple Multi-resolution Gated GNN, exhibits state-of-the-art predictive performance on the considered benchmark datasets in terms of accuracy. In addition, it is way more efficient to compute than GAT, a well-known multi-head GNN proposed in literature.

关键词： Graph Neural Network Graph Convolution Structured Data machine learning on graphs Deep learning

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AutoNE: Hyperparameter Optimization for Massive Network Embedding 19

AutoNE: Hyperparameter Optimization for Massive Network Embe...

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25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)

作者： Tu, Ke Ma, Jianxin Cui, Peng Pei, Jian Zhu, Wenwu Tsinghua Univ Beijing Peoples R China Simon Fraser Univ Burnaby BC Canada JD Com Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China

ISBN: (纸本)9781450362016

Network embedding (NE) aims to embed the nodes of a network into a vector space, and serves as the bridge between machine learning and network data. Despite their widespread success, NE algorithms typically contain a large number of hyperparameters for preserving the various network properties, which must be carefully tuned in order to achieve satisfactory performance. Though automated machine learning (AutoML) has achieved promising results when applied to many types of data such as images and texts, network data poses great challenges to AutoML and remains largely ignored by the literature of AutoML. The biggest obstacle is the massive scale of real-world networks, along with the coupled node relationships that make any straightforward sampling strategy problematic. In this paper, we propose a novel framework, named AutoNE, to automatically optimize the hyperparameters of a NE algorithm on massive networks. In detail, we employ a multi-start random walk strategy to sample several small sub-networks, perform each trial of configuration selection on the sampled sub-network, and design a meta-leaner to transfer the knowledge about optimal hyperparameters from the sub-networks to the original massive network. The transferred meta-knowledge greatly reduces the number of trials required when predicting the optimal hyperparameters for the original network. Extensive experiments demonstrate that our framework can significantly outperform the existing methods, in that it needs less time and fewer trials to find the optimal hyperparameters.

关键词： Network Representation learning Network Embedding Automated machine learning Meta-learning Hyperparameter Optimization machine learning on graphs

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An Untrained Neural Model for Fast and Accurate Graph Classification 32nd

An Untrained Neural Model for Fast and Accurate Graph Classi...

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Navarin, Nicolo Pasa, Luca Gallicchio, Claudio Sperduti, Alessandro Univ Padua Via Trieste 63 I-35121 Padua Italy Univ Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa Italy Univ Trento DISI Trento Italy

ISBN: (纸本)9783031442155;9783031442162

Recent works have proven the feasibility of fast and accurate time series classification methods based on randomized convolutional kernels [5,32]. Concerning graph-structured data, the majority of randomized graph neural networks are based on the Echo State Network paradigm in which single layers or the whole network present some form of recurrence [7,8]. This paper aims to explore a simple form of a randomized graph neural network inspired by the success of randomized convolutions in the 1-dimensional domain. Our idea is pretty simple: implement a nofrills convolutional graph neural network and leave its weights untrained. Then, we aggregate the node representations with global pooling operators, obtaining an untrained graph-level representation. Since there is no training involved, computing such representation is extremely fast. We then apply a fast linear classifier to the obtained representations. We opted for LS-SVM since it is among the fastest classifiers available. We show that such a simple approach can obtain competitive predictive performance while being extremely efficient both at training and inference time.

关键词： Graph Neural Network Graph Convolution Reservoir Computing Structured Data machine learning on graphs Deep Randomized Neural Networks

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Backpropagation-free Graph Neural Networks 22

Backpropagation-free Graph Neural Networks

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22nd IEEE International Conference on Data Mining (ICDM)

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

ISBN: (纸本)9781665450997

We propose a class of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation in hardware. The base component of our architecture is a generalization of Gated Linear Networks which 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, selects 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 neighbouring 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 hyper-planes or on prototypes. A theoretical result on the expressiveness of the proposed models is also reported. We experimented our backpropagation-free graph convolutional neural architectures 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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Compact Graph Neural Network Models for Node Classification 22

Compact Graph Neural Network Models for Node Classification

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37th Annual ACM Symposium on Applied Computing

作者： Pasa, Luca Navarin, Nicolo Sperduti, Alessandro Univ Padua Dept Math Padua Italy

ISBN: (纸本)9781450387132

Recent research on graph convolutional networks tend to increase the complexity and non-linearity of graph convolution operators. Many of these operators result in models exploiting a huge number of learnable parameters, that have to be tuned during the training phase. This aspect makes the application of these approaches on huge datasets challenging due to the considerable computational time required by the training phase. On the other hand, having a huge number of parameters limits the applicability of the models in many node classification problems, in particular in the semisupervised setting where the number of samples for which the target is known is limited to a few dozen nodes. In this paper, we propose a simple and efficient operator dubbed Compact Multi-head Exponential Graph Convolution (CM-EGC). To limit the number of learnable parameters, the proposed model exploits a compact and structurally meaningful representation of the input node features resorting to truncated SVD matrix decomposition, while the expressiveness of the model is ensured by adopting a multi-head attention-based gating mechanism. We evaluated the CM-EGC on semi-supervised and fully-supervised node classification tasks considering 3 well-known benchmark datasets. The results show that even using an extremely compact model, the classification performance of the proposed approach is comparable and sometimes better than the state of the art.

关键词： Graph Neural Network Graph Convolution Structured Data machine learning on graphs Deep learning

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Traffic speed prediction in the Lyon area using DCRNN 11

Traffic speed prediction in the Lyon area using DCRNN

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Modeles and Analyse des Reseaux: Approches Mathematiques and Informatiques - 11th Conference on Network Modeling and Analysis, MARAMI 2020

作者： Mensi, Fabio Furno, Angelo Cazabet, Remy ENS de Lyon CNRS IXXI Lyon France Univ. Lyon Univ. Gustave Eiffel LICIT UMR T9401 Lyon France Univ de Lyon CNRS Université Lyon 1 LIRIS UMR5205 Villeurbanne France

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Unsupervised framework for evaluating and explaining structural node embeddings of graphs

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JOURNAL OF COMPLEX NETWORKS 2024年第2期12卷 cnae003-cnae003页

作者： Dehghan, Ashkan Siuta, Kinga Skorupka, Agata Betlen, Andrei Miller, David Kaminski, Bogumil Pralat, Pawel Toronto Metropolitan Univ Dept Math 350 Victoria St Toronto ON M5B 2K3 Canada SGH Warsaw Sch Econ Al Niepodleglosci 162 PL-02554 Warsaw Poland Patagona Technol Pickering ON Canada

An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes' neighbourhood. For classical node embeddings, there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this article, we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information, the user gets a level of explainability to an otherwise complex black-box embedding algorithm.

关键词： graph embeddings structural similarity machine learning on graphs explainable AI node structural embeddings role-based embeddings network representation learning

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