Uniform Pooling for Graph Networks

Qin, Jian; Liu, Li; Shen, Hui; Hu, Dewen

doi:10.3390/app10186287

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…The only trainable parameter of SAGPool is the weight matrix of the employed graph convolution layer. Currently, graph pooling layers are mainly used for learning coarse graph embeddings to facilitate graph classification [29]. Few attempts have been made to introduce graph pooling techniques in the field of transportation research.…”

Section: A Gdl In Transportationmentioning

confidence: 99%

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Tang

Wei

et al. 2023

IEEE Systems Journal

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The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this paper, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, UK. By developing a two-stage graph deep learning (GDL) model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error (MAE) of only 11.18 and a root mean square error (RMSE) of 19.49; most importantly, the overall system resilience is maintained at 70% in the meantime. This paper provides an alternative perspective for dealing with the well-known 'efficiency-resilience' dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

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Section: A Gdl In Transportationmentioning

confidence: 99%

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Tang

Wei

et al. 2023

IEEE Systems Journal

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show abstract

“…Kolouri et al (2020) suggest the embedding preserving Wasserstein distance with linear complexity. Qin et al (2020) presents one more graph pooling technique that uniformly aggregates neighborhood. Baldini, Martino & Rizzi (2020) embeds maximal cliques to preserve structural similarities between graphs.…”

Section: Subgraph (And Graph) Embeddingmentioning

confidence: 99%

Survey on graph embeddings and their applications to machine learning problems on graphs

Makarov

Kiselev

Nikitinsky

et al. 2021

PeerJ Computer Science

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Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

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Graph pooling in graph neural networks: methods and their applications in omics studies

Wang,

Hou,

Sheng

et al. 2024

Artif Intell Rev

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Uniform Pooling for Graph Networks

Cited by 5 publications

References 29 publications

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Survey on graph embeddings and their applications to machine learning problems on graphs

Graph pooling in graph neural networks: methods and their applications in omics studies

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