Graph transformer networks based text representation

Mei, Xin; Cai, Xiaoyan; Yang, Linlin; Wang, Nanxin

doi:10.1016/j.neucom.2021.08.032

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…After text pre-processing, data mining, statistical analysis and other methods can be used to analyze the processed structured data in depth. Text data is generally unstructured, and it needs to be converted into structured data by text mining theory and technology, that is, text representation (Mei et al, 2021). Text representation methods include vector space-based models, statistics-based methods, topic-based methods, and word vector representation methods.…”

Section: Text Representation Methods In Text Miningmentioning

confidence: 99%

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Zeng

Zhong

2022

Journal of Organizational and End User Computing

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Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. This research has important practical significance for MSA of online product information in social media.

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Section: Text Representation Methods In Text Miningmentioning

confidence: 99%

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Zeng

Zhong

2022

Journal of Organizational and End User Computing

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“…Graph Neural Networks (GNN) deal with applying deep learning to graphical data structures. GNNs have several applications such as combinatorial optimizations, neural machine translation, protein-protein interactions, drug discovery [27][28][29][30][31][32][33][34]. Recently graph-based approaches have been used for multi-omics integration.…”

Section: Graph Based Learning Approachesmentioning

confidence: 99%

Graph Contrastive Learning for Multi-omics Data

Rajadhyaksha,

Chitkara

2023

Preprint

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Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics data classification.

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“…A data structure can be stored in memory as a graph representation using linked lists [27], [28]. The algorithm used to define the relationships and storage of nodes across multiple diagrams is represented by as shown in Figure 6.…”

Section: The Fractal Models Storage Algorithmmentioning

confidence: 99%

New efficient fractal models for MapReduce in OpenMP parallel environment

Khudhair¹,

Rabee²,

AL-Rammahi³

2023

Bulletin EEI

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Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.

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Graph transformer networks based text representation

Cited by 16 publications

References 9 publications

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Graph Contrastive Learning for Multi-omics Data

New efficient fractal models for MapReduce in OpenMP parallel environment

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