Relational graph convolutional networks: a closer look

Thanapalasingam, Thiviyan; Berkel, Lucas van; Bloem, Peter; Groth, Paul

doi:10.7717/peerj-cs.1073

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…In this paper, Relational Graph Convolutional Network (RGCN) [35] is selected as a neural network model for dealing with graph data with complex relationships. There are many advantages to using RGCN for OSNs processing.…”

Section: E-botrgcn Model Architecturementioning

confidence: 99%

A Social Bot Recognition Method Combing Emojis Information

Wang,

Wang

et al. 2024

Preprint

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The number of social bots in OSNs(Online Social Networks) ,has increased significantly, which brings great risks to social network security. Aiming at the problem of insufficient emoji usage in the existing social bot detection, this paper proposes a social account representation method combining emoji information and is used for social bot detection. The emojiswitch library was used to replace the text of emoji, and the word embedding model RoBERTa was used to obtain the tweet features. User description, user tweets, user numerical properties, user categorical properties and user relationship are combined to realize the complete emoji of social accounts. The effectiveness of the proposed method is verified based on the public data set.

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Section: E-botrgcn Model Architecturementioning

confidence: 99%

A Social Bot Recognition Method Combing Emojis Information

Wang,

Wang

et al. 2024

Preprint

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“…Firstly, for the Bipartite Graph Convolutional Network Algorithm, the main complexity comes from the update of node embeddings at each layer ( Thanapalasingam et al, 2022 ). Specifically, each layer’s embedding update involves the multiplication of the propagation matrix and the embedding matrix, with a time complexity of , where L is the network depth, is the number of nodes, and is the node feature dimension.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization

Shen,

Jiang

2024

PeerJ Computer Science

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Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users’ book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children’s book recommendation systems.

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“…Code description and source code graphs can be transformed into directed and labeled multi-graphs. We use Relational Graph Convolutional Network (RGCN) 58,59 . RGCN is a kind of GNN to learn the node embedding from directed and labeled multi-graphs.…”

Section: /19mentioning

confidence: 99%

Efficient Deep Learning based Code Retrieval using Unified Graph Structure and Semantic Graph Matching Encoder

Bashir

Rahman

2023

Preprint

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Computer programmers frequently use code retrieval systems to reuse existing code snippets from open-source repositories. These systems search a large collection of code snippets to retrieve the most relevant ones based on a text query. However, capturing semantic information between user queries and code snippets presents a key challenge in this task. Previous works have primarily used natural language processing (NLP) approaches to calculate relevance, but these approaches cannot capture deep structural and semantic dependencies between queries and snippets. In this study, we proposed a novel approach called Deep Graph-Based Semantic Representation and Matching (DeepGSR) for code retrieval. Our approach transforms queries and code snippets into a unified graph-structure representation and then retrieves relevant code snippets using a novel graph matching and retrieval approach. DeepGSR learns fine-grained relevance between queries and code snippets using cross-attention-based semantic matching operators, in addition to extracting structural information. We evaluated our approach using two publicly available code retrieval datasets, and the experiments demonstrate that DeepGSR efficiently retrieves relevant code snippets for a given query and significantly outperforms existing code retrieval approaches.

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Relational graph convolutional networks: a closer look

Cited by 14 publications

References 30 publications

A Social Bot Recognition Method Combing Emojis Information

A Social Bot Recognition Method Combing Emojis Information

Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization

Efficient Deep Learning based Code Retrieval using Unified Graph Structure and Semantic Graph Matching Encoder

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