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Graph neural link predictor based on cycle structure

CAAI Transactions on Intelligence Technology 2025年第2期10卷 615-632页

作者： Yanlin Yang Zhonglin Ye Lei Meng Mingyuan Li Haixing Zhao College of Computer Qinghai Normal UniversityXiningQinghaiChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application XiningQinghaiChina

Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the *** cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct *** a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some ***,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be ***,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural ***,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure ***,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is ***,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.

关键词： cycle structure higher-order structure link prediction multi-information interactions neural network

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DeepHGNN: A Novel Deep Hypergraph Neural Network

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Chinese Journal of Electronics 2022年第5期31卷 958-968页

作者： LIN Jingjing YE Zhonglin ZHAO Haixing FANG Lusheng College of Computer Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining Urban Vocational & Technical College

With the development of deep learning,graph neural networks(GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured data. In many real-world networks, the relations between objects are complex and go beyond *** is a flexible modeling tool to describe intricate and higher-order correlations. The researchers have been concerned how to develop hypergraph-based neural network model. The existing hypergraph neural networks show better performance in node classification tasks and so on, while they are shallow network because of oversmoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel deep hypergraph neural network(Deep HGNN). We design Deep HGNN by using the technologies of sampling hyperedge, residual connection and identity mapping, residual connection and identity mapping bring from graph convolutional neural networks. We evaluate Deep HGNN on two visual object datasets. The experiments show the positive effects of Deep HGNN, and it works better in visual object classification tasks.

关键词： Deep neural networks Graph neural network Hypergraph neural network Deep hypergraph neural network

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Knowledge graph construction of traditional tibetan medicine formulas 5

Knowledge graph construction of traditional Tibetan medicine...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhuoma, Cuo Jiayang, Dongzhou Cai, Rangjia Duojie, Cairang The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510668621

In order to intuitively and succinctly reflect information, such as the dosage form and components of traditional tibetan medicine prescriptions, and the correlation information between formulas and formulas, between formulas and traditional tibetan medicine, we have collected and collated a certain amount of traditional tibetan medicine formula data in this article, and constructed a knowledge map of traditional tibetan medicine formulas. Through the construction of this atlas, traditional tibetan medicine workers can intuitively display various direct or indirect related information of the formula, so as to facilitate a deeper understanding of the hidden knowledge in the formula information. © 2023 SPIE.

关键词： Knowledge graph

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tibetan Question Intention Classification Based on Feature Perception of Question Words 4

Tibetan Question Intention Classification Based on Feature P...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Quecairang, Guan Maojia, Yang Te, Rou Chajia, Se Zhenjiacuo, Ci Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9798331531225

Question intent classification is an important part of the tibetan Q&A system. It is a challenging task to use deep learning methods to classify the intent of tibetan questions. This paper aims to: (1) The traditional tibetan question intent classification scheme is too coarse, which is not conducive to the problem intent recognition task in the question answering system. (2) The tibetan question intent type is highly dependent on the local spatial characteristics with interrogative virtual words as the core, which leads to the problem of poor classification effect, and a novel tibetan question intent classification scheme is proposed, and each question intent type is defined. On this basis, through a detailed analysis of the semantic features and writing rules of tibetan interrogators, a semantic information labeling algorithm for tibetan interrogators was designed, and an BERT_RCNN intentional classification model for tibetan questions was trained by combining the advantages of tibetan Pre-Training (TiBERT) and recurrent convolutional neural network. The F1 value of this method can reach 94.87% on the test set, which can accurately identify user needs, compress the search space and optimize model decision-making, and significantly improve the performance of the tibetan Q&A system. © 2024 IEEE.

关键词： Question answering

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A Range-Based Node Localization Scheme for UWASNs Considering Noises and Aided With Neurodynamics Model

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2168-2170页

作者： Lijuan Wang Xiujuan Du Chong Li School of Computer Qinghai Normal UniversityQinghai Provincial Key Laboratory of IoTXining 810008 the State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810008 Academy of Plateau Science and Sustainability Xining 810008China

Dear Editor,This letter focuses on the node localization problem in underwater acoustic sensor networks(UWASNs) with the time-dependent property and various noise disturbances. A range-based localization scheme aided with an integral-feedback-based neurodynamics(IND)model is proposed and referred to as IND-RS, which has the stability against the internal perturbations encountered during the solving process.

关键词： dynamics underwater networks

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Modeling and Analysis of Matthew Effect Under Switching Social Networks via Distributed Competition

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1311-1314页

作者： Mei Liu Suibing Li Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China IEEE

Dear editor,This letter models and analyzes the Matthew effect under switching social networks via distributed competition.A competitive strategy is leveraged to trace the evolution of individuals in social systems under the Matthew *** addition,a consensus filter is utilized to decentralize the model with undirected graphs used to describe the interactions among individuals in social ***,a term is added to the dynamic consensus filter to compensate for the influence of the switching of communication networks on the *** this letter,the convergence of the proposed Matthew effect model is theoretically proved,and simulative experiments are conducted to verify the availability of the *** particular,this letter points out the state and evolution of each individual in social systems with distributed competition,and the influence of the social environment on the development of individuals is also intuitively displayed.

关键词： effect. letter intuitive

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Construction and Characteristic Analysis of Two-Layer Complex Network Model

Construction and Characteristic Analysis of Two-Layer Comple...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Tang, Chunyang Ye, Zhonglin Zhao, Haixing Xiao, Yuzhi Bai, Libing College of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

ISBN: (纸本)9789819708543

To characterize the homogeneity and heterogeneity between nodes or connecting edges, we considered the multi-relationship attributes of nodes and constructed six dual-layer network models. Firstly, based on node homogeneity, we constructed three homogeneous two-layer network models (GEE,R), (GWW,R) and (GBB,R) using the same generation algorithm for intra-layer connections and random connections for inter-layer connections. Secondly, based on node heterogeneity, we constructed three heterogeneous two-layer network models (GEW,R), (GBE,R) and (GBW,R) using different generation algorithms for intra-layer connections and random connections for inter-layer connections. Then, we derived the average degree analytical expressions and node degree distribution analytical expressions for the six two-layer networks. Finally, according to the differences in inter-layer connection probabilities, we depict the experiments with node degree distribution. Experiments have shown that the node degree distribution of (GEW,R) networks exhibits a bimodal characteristic, while the node degree distribution of (GBE,R) networks presents both Power-law and unimodal mixed characteristics. Overall, this study provides insights into the characteristics of different two-level network models and their application in modeling complex systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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Speech emotion recognition based on CNN-MGU-attention

Speech emotion recognition based on CNN-MGU-attention

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2023 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2023

作者： Wang, Yanni Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9781510666351

Speech is the most natural way of human communication, carrying a wealth of emotional information, and emotion is an important part of human cognitive process, so speech emotion recognition (SER) has become an important research direction in the field of pattern recognition. SER technology enables computers to understand human emotions, which is an indispensable step to realize artificial intelligence. However, there are some problems in the field of SER, such as the lack of effective emotion feature set and effective emotion recognition model. Starting from improving the recognition performance of SER model, this paper builds a new model CNN-MGU-Attention on the basis of deep neural network to improve the performance of speech emotion recognition system. The model is composed of convolutional neural network (CNN), minimum gated unit (MGU) and attention mechanism. Firstly, MGU can simplify the gate structure to the smallest possible state, and has far fewer parameters than long Short-Term memory (LSTM) and gated unit (GRU), which greatly reduces the training complexity and improves the training speed. Secondly, the attention mechanism was used to learn the correlation degree between the input and output sequences of the model, so as to pay more attention to the effective information. Finally, the Softmax layer was used to classify the sentiment. Experiments show that the CNN-MGU-Attention network model proposed in this study achieves an average recognition accuracy of 88.90%and 86.21% on the CASIA and EMODB databases respectively, which achieves better recognition performance compared with previous research results. © 2023 SPIE.

关键词： Deep neural networks

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Numerical‐discrete‐scheme‐incorporated recurrent neural network for tasks in natural language processing

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1415-1424页

作者： Mei Liu Wendi Luo Zangtai Cai Xiujuan Du Jiliang Zhang Shuai Li School of Information Science and Engineering Lanzhou UniversityLanzhouChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXiningChina Department of Electronic and Electrical Engineering The University of SheffieldSheffieldUK

A variety of neural networks have been presented to deal with issues in deep learning in the last *** the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive *** research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential *** connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical *** RNN is regarded as a discretisation of the forward Euler *** the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its *** experiments are conducted to evaluate its performance on statistical language models and emotion analysis *** noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.

关键词： deep learning natural language processing neural network text analysis

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Semantic Tagging Algorithm for tibetan La Case Function Words Oriented to Classification of La Case 4

Semantic Tagging Algorithm for Tibetan La Case Function Word...

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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Ban, Mabao Duo, Jiecairang Zhang, Rui Yang, Maojia Cai, Rangjia The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining China Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province Xining China

ISBN: (纸本)9798350306323

Automatic classification of tibetan La case () is a challenging and significant task in tibetan natural language processing. Compared with other short text classification tasks, the automatic classification of La case depend more on the semantic information of context and function words of La case. Based on the definition about La case classification task, this paper designs a semantic labeling algorithm for tibetan La case function words by analyzing their usage and semantic features. When training La case classification model, calling this algorithm can integrate the semantic information about La case function words, thus increasing the diversity of input features and enriching the semantic information of embedded vectors. Experiments on eight commonly used short text classification models show that the classification accuracy (ACC) after fusing the semantic information on La case function words is improved by 2.1 percentage points on average, which verifies the effectiveness and universality of the algorithm. © 2023 IEEE.

关键词： Semantics

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