Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

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Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

10.20944/preprints202011.0511.v2 ◽

2020 ◽

Author(s):

Liang Zhang ◽

Jingqun Li ◽

Bin Zhou ◽

Yan Jia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Network Architectures ◽

Convolutional Networks ◽

Effective Manner ◽

The Media ◽

Deep Learning Network ◽

Graph Neural Networks ◽

Rumor Detection

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

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Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

10.5753/eniac.2020.12161 ◽

2020 ◽

Author(s):

Douglas Meneghetti ◽

Reinaldo Bianchi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Communication Channels ◽

Neural Network Architecture ◽

Graph Representations ◽

Labeled Graph ◽

Multiple Agent ◽

Multi Agent ◽

Graph Neural Networks

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

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SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

Economy of Ukraine ◽

10.15407/economyukr.2020.10.054 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 54-62

Author(s):

Oleksii VASYLIEV ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Statistical Data ◽

Activation Function ◽

Decision Making Process ◽

Neural Network Architecture ◽

Acceptable Accuracy ◽

The Neural Network ◽

Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

ACM Transactions on Graphics ◽

10.1145/3450284 ◽

2021 ◽

Vol 40 (3) ◽

pp. 1-13

Author(s):

Lumin Yang ◽

Jiajie Zhuang ◽

Hongbo Fu ◽

Xiangzhi Wei ◽

Kun Zhou ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Large Scale ◽

State Of The Art ◽

Semantic Segmentation ◽

Structure Information ◽

Graph Neural Networks ◽

Node Labels ◽

Point Level

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Journal of Fluid Mechanics ◽

10.1017/jfm.2016.615 ◽

2016 ◽

Vol 807 ◽

pp. 155-166 ◽

Cited By ~ 274

Author(s):

Julia Ling ◽

Andrew Kurzawski ◽

Jeremy Templeton

Keyword(s):

Neural Network ◽

Neural Networks ◽

Reynolds Stress ◽

Network Architecture ◽

Eddy Viscosity ◽

Deep Neural Networks ◽

Test Cases ◽

Neural Network Architecture ◽

Stress Anisotropy ◽

Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

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Preintegration Lateral Inhibition Enhances Unsupervised Learning

Neural Computation ◽

10.1162/089976602320264033 ◽

2002 ◽

Vol 14 (9) ◽

pp. 2157-2179 ◽

Cited By ~ 34

Author(s):

M. W. Spratling ◽

M. H. Johnson

Keyword(s):

Neural Network ◽

Lateral Inhibition ◽

Network Architecture ◽

Network Architectures ◽

Neural Network Architecture ◽

Neurophysiological Data ◽

Perceptual Representations ◽

Neural Network Architectures ◽

The Right ◽

To Receive

A large and influential class of neural network architectures uses postintegration lateral inhibition as a mechanism for competition. We argue that these algorithms are computationally deficient in that they fail to generate, or learn, appropriate perceptual representations under certain circumstances. An alternative neural network architecture is presented here in which nodes compete for the right to receive inputs rather than for the right to generate outputs. This form of competition, implemented through preintegration lateral inhibition, does provide appropriate coding properties and can be used to learn such representations efficiently. Furthermore, this architecture is consistent with both neuroanatomical and neurophysiological data. We thus argue that preintegration lateral inhibition has computational advantages over conventional neural network architectures while remaining equally biologically plausible.

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Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

EPJ Web of Conferences ◽

10.1051/epjconf/202022602020 ◽

2020 ◽

Vol 226 ◽

pp. 02020

Author(s):

Alexey V. Stadnik ◽

Pavel S. Sazhin ◽

Slavomir Hnatic

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Vision ◽

Performance Analysis ◽

Object Detection ◽

Real Time ◽

Network Architecture ◽

Neural Network Architecture ◽

Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

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Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks

10.1101/099879 ◽

2017 ◽

Cited By ~ 4

Author(s):

Joe Paggi ◽

Andrew Lamb ◽

Kevin Tian ◽

Irving Hsu ◽

Pierre-Louis Cedoz ◽

...

Keyword(s):

Neural Network ◽

Network Architecture ◽

Transcriptional Activity ◽

Cell Types ◽

Massively Parallel ◽

Neural Network Architecture ◽

Regulatory Regions ◽

Convolutional Networks ◽

Transcriptional Regulatory ◽

Reporter Assays

AbstractMassively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.

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