Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Ying, Rex; He, Ruining; Chen, Kaifeng; Eksombatchai, Pong; Hamilton, William L.; Leskovec, Jure

doi:10.1145/3219819.3219890

Cited by 2,993 publications

(1,839 citation statements)

References 18 publications

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“…Graph Convolution Network (Kipf and Welling, 2016), Graph Attention Networks (Velickovic et al, 2018) and Graph Embeddings (Cai et al, 2017) have received considerable research attention. This is due to the fact that many real-world problems in complex systems, such as recommendation systems (Ying et al, 2018), social networks and biological networks (Fout et al, 2017) etc, can be modelled as machine learning tasks over large networks. Graph Convolutional Network (GCN) was proposed to deal with graph structures.…”

Section: Introductionmentioning

confidence: 99%

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

Pal¹,

Selvakumar²,

Sankarasubbu³

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It is observed that most MLTC tasks, there are dependencies or correlations among labels. Existing methods tend to ignore the relationship among labels. In this paper, a graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The graph attention network uses a feature matrix and a correlation matrix to capture and explore the crucial dependencies between the labels and generate classifiers for the task. The generated classifiers are applied to sentence feature vectors obtained from the text feature extraction network(BiLSTM) to enable end-to-end training. Attention allows the system to assign different weights to neighbor nodes per label, thus allowing it to learn the dependencies among labels implicitly. The results of the proposed model are validated on five real-world MLTC datasets. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.

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Section: Introductionmentioning

confidence: 99%

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

Pal¹,

Selvakumar²,

Sankarasubbu³

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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“…1. In this network, the potential disease-gene associations can be considered as missing links and our goal is to predict these links (Chen et al, 2005;Ying et al, 2018). The overview of our method is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

Kuwahara

Yang

et al. 2019

Preprint

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Motivation: Proper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. Results: Here, we propose a novel graph convolutional networkbased disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. Availability: The main program and the data are available at https: //github.com/lykaust15/Disease_gene_prioritization_GCN.

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“…• Mean Reciprocal Rank (MRR) calculates the reciprocal rank of a query concept's true parent. We follow [55] and use a scaled version of MRR in the below equation:…”

Section: Expanding Mag Field-of-study Taxonomymentioning

confidence: 99%

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

Shen

Xiong

et al. 2020

Proceedings of the Web Conference 2020

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Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of ⟨query concept, anchor concept⟩ pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.

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Graph Convolutional Neural Networks for Web-Scale Recommender Systems

Cited by 2,993 publications

References 18 publications

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

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