Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

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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Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Machine Learning and Knowledge Extraction ◽

10.3390/make3010005 ◽

2021 ◽

Vol 3 (1) ◽

pp. 84-94

Author(s):

Liang Zhang ◽

Jingqun Li ◽

Bin Zhou ◽

Yan Jia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Network Architectures ◽

Neural Network Architecture ◽

Convolutional Networks ◽

Effective Manner ◽

Deep Learning Network ◽

Graph Neural Networks ◽

Rumor Detection

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.v1 ◽

2020 ◽

Author(s):

Liang Zhang ◽

Jingqun Li ◽

Bin Zhou ◽

Yan Jia

Keyword(s):

Neural Networks ◽

Numerical Experiments ◽

State Of The Art ◽

Fake News ◽

Wide Spread ◽

Convolutional Networks ◽

The Media ◽

Graph Neural Networks ◽

Rumor Detection ◽

Simple Network

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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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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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A mixed-scale dense convolutional neural network for image analysis

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1715832114 ◽

2017 ◽

Vol 115 (2) ◽

pp. 254-259 ◽

Cited By ~ 60

Author(s):

Daniël M. Pelt ◽

James A. Sethian

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Network Architecture ◽

Training Data ◽

Network Architectures ◽

Feature Maps ◽

Deep Convolutional Neural Networks ◽

Single Set ◽

Reduced Risk

Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.

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CommNets: Communicating Neural Network Architectures for Resource Constrained Systems

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019909 ◽

2019 ◽

Vol 33 ◽

pp. 9909-9910

Author(s):

Prince M Abudu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Recurrent Neural Networks ◽

Network Architecture ◽

Resource Constraints ◽

Constrained Systems ◽

Detection Accuracy ◽

Network Architectures ◽

Operating Environments ◽

Hidden States

Applications that require heterogeneous sensor deployments continue to face practical challenges owing to resource constraints within their operating environments (i.e. energy efficiency, computational power and reliability). This has motivated the need for effective ways of selecting a sensing strategy that maximizes detection accuracy for events of interest using available resources and data-driven approaches. Inspired by those limitations, we ask a fundamental question: whether state-of-the-art Recurrent Neural Networks can observe different series of data and communicate their hidden states to collectively solve an objective in a distributed fashion. We realize our answer by conducting a series of systematic analyses of a Communicating Recurrent Neural Network architecture on varying time-steps, objective functions and number of nodes. The experimental setup we employ models tasks synonymous with those in Wireless Sensor Networks. Our contributions show that Recurrent Neural Networks can communicate through their hidden states and we achieve promising results.

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Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Entropy ◽

10.3390/e23111500 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1500

Author(s):

Xiangde Zhang ◽

Yuan Zhou ◽

Jianping Wang ◽

Xiaojun Lu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Objective Function ◽

Cross Entropy ◽

Personal Interest ◽

Entropy Loss ◽

Convolutional Networks ◽

The Cross ◽

Graph Neural Networks

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.

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Scalable Graph Neural Network Training

ACM SIGOPS Operating Systems Review ◽

10.1145/3469379.3469387 ◽

2021 ◽

Vol 55 (1) ◽

pp. 68-76

Author(s):

Marco Serafini

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Network ◽

Network Architectures ◽

Neural Network Training ◽

Large Graphs ◽

Graph Data ◽

Network Training ◽

Neural Network Architectures ◽

Graph Neural Networks

Graph Neural Networks (GNNs) are a new and increasingly popular family of deep neural network architectures to perform learning on graphs. Training them efficiently is challenging due to the irregular nature of graph data. The problem becomes even more challenging when scaling to large graphs that exceed the capacity of single devices. Standard approaches to distributed DNN training, like data and model parallelism, do not directly apply to GNNs. Instead, two different approaches have emerged in the literature: whole-graph and sample-based training. In this paper, we review and compare the two approaches. Scalability is challenging with both approaches, but we make a case that research should focus on sample-based training since it is a more promising approach. Finally, we review recent systems supporting sample-based training.

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Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

ACM Transactions on Information Systems ◽

10.1145/3490181 ◽

2022 ◽

Vol 40 (4) ◽

pp. 1-46

Author(s):

Hao Peng ◽

Ruitong Zhang ◽

Yingtong Dou ◽

Renyu Yang ◽

Jingyi Zhang ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Message Passing ◽

Representation Learning ◽

Graph Representations ◽

Graph Data ◽

Neighborhood Information ◽

Neighborhood Selection ◽

Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data, typically through message passing among nodes by aggregating their neighborhood information via different operations. While promising, most existing GNNs oversimplify the complexity and diversity of the edges in the graph and thus are inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this article, we propose RioGNN , a novel Reinforced, recursive, and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining relation-dependent representations. We first construct a multi-relational graph, according to the practical task, to reflect the heterogeneity of nodes, edges, attributes, and labels. To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes. A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding. Particularly, to improve the efficiency of neighbor selecting, we propose a new recursive and scalable reinforcement learning framework with estimable depth and width for different scales of multi-relational graphs. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism. Comprehensive experiments on real-world graph data and practical tasks demonstrate the advancements of effectiveness, efficiency, and the model explainability, as opposed to other comparative GNN models.

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