Whole-Graph Embedding and Adversarial Attacks for Life Sciences

Maddalena, Lucia; Giordano, Maurizio; Manzo, Mario; Guarracino, Mario Rosario

doi:10.1007/978-3-031-12515-7_1

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…Formula ( 6) cannot reflect the strength of connectivity between nodes. This article proposes an adaptive geographically weighted function for nodes to determine the relationship between weights and distances, and the calculation rule is shown in Formula (7). Wherein, d * ij stands for the Euclidean distance of nodes v i and v j , δ denotes the detection radius with the central node as the core.…”

Section: Rules For Calculating the Weight Coefficientmentioning

confidence: 99%

“…Spatio-temporal heterogeneity data (STD) [1][2][3] is the data basis for applying big data analysis technology to solve decision-making problems in urban operation and maintenance [4], oil and gas development [5], medical decision-making [6], life sciences [7] and other fields, and its accuracy detection is undoubtedly important [8,9]. Due to the stochasticity and complexity of STD in the temporal dimension and the global and local correlation in the spatial dimension, which makes detection extremely difficult, related research has also become a research hotspot for data analysts [10].…”

Section: Introductionmentioning

confidence: 99%

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A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Liu¹,

Zhang²,

Yin³

et al. 2023

Computer Systems Science and Engineering

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Spatio-temporal heterogeneous data is the database for decisionmaking in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology. Secondly, design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy. Finally, the proposed method is compared with three methods, ARIMA, T-GCN, and STGCN, in real scenarios to verify its effectiveness in terms of detection speed, detection accuracy and stability. The experimental results show that the RMSE, MAE, and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree, which are 13.82/12.08, 2.77/2.41, and 16.70/14.73, respectively. Also, it detects the shortest time of 672.31/887.36, respectively. In addition, the evaluation results are the same under different time periods of processing and complex topology environment, which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

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Section: Rules For Calculating the Weight Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Liu¹,

Zhang²,

Yin³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

Network-Based Computational Modeling to Unravel Gene Essentiality

Granata

Giordano

Maddalena

et al. 2023

Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

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Adversarial attacks on graph-level embedding methods: a case study

Giordano

Maddalena

Manzo

et al. 2022

Ann Math Artif Intell

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As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go in-depth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage.

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Whole-Graph Embedding and Adversarial Attacks for Life Sciences

Cited by 8 publications

References 47 publications

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Network-Based Computational Modeling to Unravel Gene Essentiality

Adversarial attacks on graph-level embedding methods: a case study

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