scholarly journals A Classification Method for Academic Resources Based on a Graph Attention Network

2021 ◽  
Vol 13 (3) ◽  
pp. 64
Author(s):  
Jie Yu ◽  
Yaliu Li ◽  
Chenle Pan ◽  
Junwei Wang
Keyword(s):  
Neural Networks ◽  
Citation Networks ◽  
Classification Methods ◽  
Attention Network ◽  
Resource Classification ◽  
Semantic Relevance ◽  
Graph Neural Networks ◽  
Academic Resource ◽  
Academic Resources

Classification of resource can help us effectively reduce the work of filtering massive academic resources, such as selecting relevant papers and focusing on the latest research by scholars in the same field. However, existing graph neural networks do not take into account the associations between academic resources, leading to unsatisfactory classification results. In this paper, we propose an Association Content Graph Attention Network (ACGAT), which is based on the association features and content attributes of academic resources. The semantic relevance and academic relevance are introduced into the model. The ACGAT makes full use of the association commonality and the influence information of resources and introduces an attention mechanism to improve the accuracy of academic resource classification. We conducted experiments on a self-built scholar network and two public citation networks. Experimental results show that the ACGAT has better effectiveness than existing classification methods.

scholarly journals Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks

2020 ◽  
Author(s):  
Doruk Pancaroglu
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Face Recognition ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Performance Metrics ◽  
Fine Art ◽  
Classification Methods

Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.

scholarly journals Skin Cancer Classification using Convolutional Neural Networks: Systematic Review (Preprint)

2018 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Jochen Sven Utikal ◽  
Dirk Schadendorf ◽  
Carola Berking ◽  
...  
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Skin Lesions ◽  
Data Sets ◽  
Classification Methods ◽  
Public Data

BACKGROUND State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outside the hospital via installation on mobile devices. To our knowledge, at present, there is no review of the current work in this research area. OBJECTIVE This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. METHODS We searched the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. RESULTS We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large data set and then optimize its parameters to the classification of skin lesions are both the most common methods as well as display the best performance with the currently available limited data sets. CONCLUSIONS CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use non-public data sets for training and/or testing, thereby making reproducibility difficult.

scholarly journals EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals

2021 ◽  
Author(s):  
Andac Demir ◽  
Toshiaki Koike-Akino ◽  
Ye Wang ◽  
Masaki Haruna ◽  
Deniz Erdogmus
Keyword(s):  
Neural Networks ◽  
Eeg Signals ◽  
Graph Neural Networks ◽  
Electroencephalogram Eeg

scholarly journals Graph Convolutional Networks by Architecture Search for PolSAR Image Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1404
Author(s):  
Hongying Liu ◽  
Derong Xu ◽  
Tianwen Zhu ◽  
Fanhua Shang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spatial Relations ◽  
Classification Problem ◽  
Search Method ◽  
Sample Distribution ◽  
Convolutional Networks ◽  
Training Samples ◽  
Graph Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods.

scholarly journals Social Recommendation System Based on Hypergraph Attention Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhongxiu Xia ◽  
Weiyu Zhang ◽  
Ziqiang Weng
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Recommendation System ◽  
Recommendation Systems ◽  
High Order ◽  
Social Recommendation ◽  
Attention Network ◽  
Practical Applications ◽  
Order Relations ◽  
Graph Neural Networks

In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph’s ability to model high-order relations to capture high-order relations between users. However, because the influence of the users’ friends is different, we use the graph attention mechanism to capture the users’ attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.

scholarly journals Session-Based Recommendation with Graph Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 346-353 ◽  
Author(s):  
Shu Wu ◽  
Yuyuan Tang ◽  
Yanqiao Zhu ◽  
Liang Wang ◽  
Xing Xie ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Current Interest ◽  
Attention Network ◽  
Sequential Methods ◽  
Novel Method ◽  
Graph Neural Networks ◽  
User Actions

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

scholarly journals Multi-Channel Graph Neural Networks

2020 ◽  
Author(s):  
Kaixiong Zhou ◽  
Qingquan Song ◽  
Xiao Huang ◽  
Daochen Zha ◽  
Na Zou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Layer By Layer ◽  
The Hierarchical Structure ◽  
Multiple Characteristics ◽  
Real World Datasets ◽  
Underlying Mechanisms ◽  
Graph Neural Networks ◽  
Isomorphic Graphs

The classification of graph-structured data has be-come increasingly crucial in many disciplines. It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification applications. A straightforward way to leverage the hierarchical structure is to make use the pooling algorithms to cluster nodes into fixed groups, and shrink the input graph layer by layer to learn the pooled graphs.However, the pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the fixed clustering ignores the inherent multiple characteristics of nodes. To compensate the shrinking loss and learn the various nodes’ characteristics, we propose the multi-channel graph neural networks (MuchGNN). Motivated by the underlying mechanisms developed in convolutional neural networks, we define the tailored graph convolutions to learn a series of graph channels at each layer, and shrink the graphs hierarchically to en-code the pooled structures. Experimental results on real-world datasets demonstrate the superiority of MuchGNN over the state-of-the-art methods.

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