STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks

Min, Shengjie; Gao, Zhan; Peng, Jing; Wang, Liang; Qin, Ke; Fang, B.

doi:10.1016/j.knosys.2021.106746

Cited by 84 publications

(35 citation statements)

References 17 publications

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“…Some subsequent works integrated the attention mechanism [13,36,37], autoencoder [14,38], generative network [39,40], and other structures into the GNNs. With the vigorous development of GNN models, their applications have become more and more extensive in various fields, such as social networks [41], recommendation systems [42], life sciences [43], and so on. For unstructured data such as images, superpixels can transform images into graph structures, thus solving image-related tasks using graph neural networks [44][45][46].…”

Section: Graph Neural Networkmentioning

confidence: 99%

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Diao

Zhang

et al. 2022

Remote Sensing

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Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively.

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Section: Graph Neural Networkmentioning

confidence: 99%

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Diao

Zhang

et al. 2022

Remote Sensing

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“…Graph The joints and links between adjacent joints of the skeleton representation (a human skeletal kinematic tree) naturally remind the researchers of graph [67,66]. Graph ( , ) is a data format and also an encoding mode that can be used to represent social networks [83,74], communication networks [80,98], protein molecular networks [6,76], etc. The nodes in the graph represent individuals in the network, and the linked edges represent connections between individuals.…”

Section: Mathematical Representationmentioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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“…Compared to regular grids and Euclidean spaces, graphs are irregularly structured (i.e., irregular neighborhood relationships). Thus, many of the machine learning methods cannot directly be used on graphs to perform tasks such as classification (e.g., role of a node in a social network), prediction (e.g., whether or not a social relation exists between two nodes), and community detection (e.g., discovery of criminal groups) [147]. For example, CNNs have been applied quite extensively for image classification and segmentation.…”

Section: Deep Graph Neural Networkmentioning

confidence: 99%

Machine Learning of Spatial Data

Nikparvar

Thill

2021

IJGI

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

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STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks

Cited by 84 publications

References 17 publications

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

3D Human Motion Prediction: A Survey

Machine Learning of Spatial Data

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