Heterogeneous question answering community detection based on graph neural network

Wu, Yongliang; Fu, Yue; Xu, Jun; Yin, Hu; Zhou, Qianqian; Liu, Dongbo

doi:10.1016/j.ins.2022.10.126

Cited by 22 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies published in the literature attempt to extract high-quality communities. Some of them used the graph partitioning techniques such as K-means to detect communities [15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

Torkaman,

Badie,

Salajegheh

et al. 2023

IJE

View full text Add to dashboard Cite

Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful.

show abstract

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

Torkaman,

Badie,

Salajegheh

et al. 2023

IJE

View full text Add to dashboard Cite

show abstract

“…Initially, research on DGNNs primarily built upon the successes achieved in static graph research, which demonstrated notable outcomes in downstream tasks including link prediction [1][2][3], node classification [4], and community detection [5,6]. To harness these capabilities for dynamic scenarios, researchers began integrating static GNN frameworks with Recurrent Neural Networks (RNNs), enabling the models to not only distill graph structures but also effectively encode temporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

A binary-domain recurrent-like architecture-based dynamic graph neural network

Chen,

Lin

2024

Auton. Intell. Syst.

View full text Add to dashboard Cite

The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.

show abstract

Improved Graph Neural Network With Graph Filtering Kernel and Generalized Nonconvex Norm Inspired by a Novel Unified Optimization Framework

Yang,

Yang

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

Heterogeneous question answering community detection based on graph neural network

Cited by 22 publications

References 43 publications

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

A binary-domain recurrent-like architecture-based dynamic graph neural network

Improved Graph Neural Network With Graph Filtering Kernel and Generalized Nonconvex Norm Inspired by a Novel Unified Optimization Framework

Contact Info

Product

Resources

About