A Deep Graph Structured Clustering Network

Li, Xunkai; Hu, Youpeng; Sun, Yaoqi; Hu, Jiang; Zhang, Jiyong; Qu, Mingli

doi:10.1109/access.2020.3020192

Cited by 19 publications

(5 citation statements)

References 25 publications

(33 reference statements)

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“…In this context nodes represent products and edges represent links between products, indicating their similarity and complementarity of Hidalgo. To improve these graph-based recommendations, clustering has proven its effectiveness in obtaining more relevant results [19][20][21]. On the other hand, studies have shown that GNNs can bring diversity to recommendations [22,23].…”

Section: Related Workmentioning

confidence: 99%

Product Space Clustering with Graph Learning for Diversifying Industrial Production

Cortial,

Albouy-Kissi,

Chausse

2024

Applied Sciences

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During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research.

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Section: Related Workmentioning

confidence: 99%

Product Space Clustering with Graph Learning for Diversifying Industrial Production

Cortial,

Albouy-Kissi,

Chausse

2024

Applied Sciences

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“…The problem of missing power data (voltage, current, or power) in the low-voltage station area can be essentially transformed into a problem of reconstruction of missing data. Deep convolutional autoencoder is an unsupervised deep learning network, which can realize the reconstruction of missing data through end-to-end learning [20][21].…”

Section: A Deep Convolutional Autoencodermentioning

confidence: 99%

A Method of Complementing Missing Power Data in Low-Voltage Stations Based on Improved Deep Convolutional Self-Encoding Network

et al. 2022

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The irregularities in the collection and transmission of user power data in the low-voltage power distribution station area have led to errors in the subsequent application analysis of the station area. In order to ensure the integrity of power data in low-voltage stations, a multi-user power missing data complement method based on improved deep convolutional self-encoding is proposed. First, according to the characteristics of the lack of multi-user power data in the low-voltage station area, the power data is formed into a spatio-temporal tensor data format that can be used for one-dimensional convolution operations. Then use the encoding and decoding capabilities of the improved deep convolutional selfencoding network to realize the reconstruction of missing data, and optimize the network structure by introducing residual learning and batch normalization (BN). Finally, based on the proposed method, two cases of random and continuous loss of user power data in a certain area are complemented. The results show that the method can accurately complete 40% of randomly missing data and 2 consecutive days of missing data. The proposed method has improved completion accuracy compared with traditional methods to varying degrees. INDEX TERMSIntelligent distribution network, low-voltage power distribution station area, deep convolutional autoencoder, residual learning, missing data completion.

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“…Recent years, many GCN variants have been applied to different graph-related research fields, such as clustering [24], [25], computer vision [26], [27], natural language processing [28], [29]and recommender systems [30], [31]. SGC [32] removes the nonlinear activation function and collapses the weight matrix between consecutive layers, which greatly simplifies calculations with similar performance as GCN [13].…”

Section: B Graph-based Cfmentioning

confidence: 99%

A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

Guo

et al. 2021

IEEE Access

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Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering. However, most GCN-based models have inferior embedding propagation mechanism, leading to low information extraction efficiency. Besides, the existing methods suffer from high computational complexity for large user-item interaction graphs. In order to solve the above problems, we propose LII-GCCF that integrates Linear transformation, Initial residual and Identity mapping into the Graph Convolutional Collaborative Filtering model. First, initial residual and identity mapping are applied to optimize the information propagation of graph convolution, which privide abundant interaction and alleviate information loss problem. Second, LII-GCCF removes the unnecessary nonlinear transformation based on the characteristics of collaborative filtering to simplify the graph convolution process. Comprehensive experiments are conducted on two public datasets, and the results demonstrate that LII-GCCF has a significant improvement over other state-of-the-art methods.INDEX TERMS Collaborative filtering, graph convolutional network, recommender systems.

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A Deep Graph Structured Clustering Network

Cited by 19 publications

References 25 publications

Product Space Clustering with Graph Learning for Diversifying Industrial Production

Product Space Clustering with Graph Learning for Diversifying Industrial Production

A Method of Complementing Missing Power Data in Low-Voltage Stations Based on Improved Deep Convolutional Self-Encoding Network

A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

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