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Inductive Relation Prediction by Disentangled Subgraph Structure

Tsinghua Science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Preserving model privacy for Federated Reinforcement Learning in Complementary Environments 23

Preserving model privacy for Federated Reinforcement Learnin...

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15th International Conference on Machine Learning and computing, ICMLC 2023

作者： Xuan, Pengyu Chen, Aiguo Sha, Zexin University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781450398411

Federated reinforcement learning (FRL) uses data from multiple partners interacting with the environment to train a global decision model while maintaining data privacy. In specific situations, it is necessary to protect not only data privacy but also model privacy. That is, our objective is to collaboratively train a global model that can be used privately and independently by the initiator. We refer to this need for model privacy preservation as model monopolization. It allows the task initiator to ensure the privacy of the model during the training process and to own the trained model alone. To the best of our knowledge, model monopolization protection in Federated Reinforcement Learning has not been studied. We propose monopolized federated reinforcement learning in complementary environments via function transformation. We also provide contribution metrics to ensure the sustainability of monopolized federated reinforcement learning and to provide a quantitative basis for task initiators to give back to task participants. The experimental results show that our method achieved model monopolization by training agents in the Grid-world. © 2023 ACM.

关键词： Reinforcement learning

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Structure-aware Table-to-Text Generation with Prefix-tuning 23

Structure-aware Table-to-Text Generation with Prefix-tuning

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Liu, Cencen Xu, Yi Yin, Wen Zheng, Dezhang University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Sichuan Chengdu China

ISBN: (纸本)9798400708190

Table-to-text generation is designed to generate descriptive natural language for structured tables that conforms to objective facts and follows the source data. The current challenge in this field is to capture the structural information of the table and improve the quality of the generated text. The existing sequence-to-sequence approach is to linearize the table, which leads to miss captured structure information and is not conducive to the model learning contextual semantics. In this paper, we introduce structural-aware self-attention, which focuses on table structure to capture cell relationships between the same row or column. In this way, the generated descriptive text can more accurately reflect the correlation between the cells in the table, discarding irrelevant information. In order to adapt the pre-trained language model to the table-to-text generation task, we introduce prefix-tuning. Traditional fine-tuning methods update all model parameters, which leads to increased training costs. In contrast, using prefix-tuning for a more lightweight approach can improve model performance considerably. Attaching continuous prompts to tokens helps the model better understand the structure and semantics of the input sequence. All of our models are extended based on T5 and have strong competitiveness in the ToTTo dataset and Hitab dataset compared with several classical baselines. © 2023 ACM.

关键词： Semantics

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua Science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Some Thoughts on the Classification Standard of Intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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PDA-GCN: Predicting Piwi-interacting RNA-Disease Associations Based on Graph Convolution Network

PDA-GCN: Predicting Piwi-interacting RNA-Disease Association...

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IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Honglin Su Hui Gao Trusted Cloud Computing and Big Data Engineering Lab University of Electronic Science and Technology of China Sichuan China

Piwi-interacting RNAs (piRNAs) play a significant role in multiple complex human diseases. In order to understand disease pathogenesis at the molecular level, identifying potential associations between piRNAs and diseases accurately is very critical. The computational methods provide a cost-reducing strategy for biological wet experiments. Several computational methods have been proposed, however, most of them still adopted traditional machine learning methods. It is still a challenge for them to deal with the complex and non-linear data. Identifying associations between piRNA-disease is a link prediction task in the heterogeneous network constructed by piRNAs, diseases, and the existing verified associations between them. In this study, a computational method named PDA-GCN is proposed based on Graph Convolution Network (GCN). GCN performance well in aggregate complex information hidden in the heterogeneous network. PDA-GCN adopt 2-layer GCNs to extract features form three aspects. Experiment results show that PDA-GCN achieves superior performance comparing with state-of-the-art methods.

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The research of human interaction recognition based on fusion features of key frame feature library

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International Journal of Information and Communication Technology 2021年第1期18卷 57-69页

作者： Zhang, Haiyan Gao, Shangbing Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China Nanjing Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilised to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others. © 2021 Inderscience Enterprises Ltd.

关键词： K-means clustering

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Relation-Based Multi-Teacher Knowledge Distillation

Relation-Based Multi-Teacher Knowledge Distillation

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International Joint Conference on Neural Networks (IJCNN)

作者： Yutian Wang Yi Xu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traditional knowledge distillation adopts one teacher model to instruct the training of a lightweight student model. To improve the performance of knowledge distillation, multi-teacher knowledge distillation utilizing multi-party knowledge to guide model learning was proposed. However, existing methods mainly use the summation-result of teacher models of a single sample to integrate multi-party knowledge directly, ignoring the relation knowledge among samples. In this paper, we propose a novel multi-teacher knowledge distillation method, which utilizes the data relation knowledge to allocate weights for teachers adaptively. To obtain the data relation knowledge, we design output-based strategy and feature-based strategy, which would help to allocate more weight for the teacher who has better learned the data relation knowledge. Extensive experiments have demonstrated the performance and efficiency of our proposed multi-teacher knowledge distillation method.

关键词： Training Adaptation models Neural networks

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First-Order Meta-Learning in Node Classification with Graph Convolutional Network 4

First-Order Meta-Learning in Node Classification with Graph ...

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4th International Conference on Artificial Intelligence and big data, ICAIBD 2021

作者： Cao, Jing Xu, Yi Song, Xuening School of Information and Software Engineering University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9780738131702

In recent years, research on graph representation learning within graph neural networks has made great progress. Among them, graph convolutional neural networks (GCN), which is based on spectral domain, have drawn great attention and become one of the mainstream graph embedding approaches. However, few-shot learning on graphs has little related work, as we know. Toward this problem, we propose a meta-learning method on graphs, FOML-GCN (First order meta-learning on GCN), aimed to be applied in various kinds of models of graph convolutional networks. As many common approaches in meta-learning, FOML-GCN involves two phases, meta-training and meta-testing. By learning the potential Knowledge from a small number of samples in existing classes during meta-training, our method enhances the ability in node classification of the base model by training within new few data that only appear in the meta-testing step. The experiment on two benchmark datasets shows that our FOML-GCN performs better than base GCN and other models of graph network in few-shot node classification tasks. © 2021 IEEE.

关键词： Graph neural networks

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