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Comprehensive Architecture Search for Deep Graph Neural Networks

IEEE Transactions on Big Data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

arXiv

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arXiv 2022年

作者： Wang, Zixiao Guo, Yuluo Zhao, Jin Zhang, Yu Yu, Hui Liao, Xiaofei Wang, Biao Yu, Ting National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Complex networks

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Reconciling Selective Logging and Hardware Persistent Memory Transaction

Reconciling Selective Logging and Hardware Persistent Memory...

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IEEE Symposium on High-Performance Computer Architecture

作者： Chencheng Ye Yuanchao Xu Xipeng Shen Yan Sha Xiaofei Liao Hai Jin Yan Solihin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China North Carolina State University Raleigh North Carolina USA University of Central Florida Florida USA

Log creation, maintenance, and its persist ordering are known to be performance bottlenecks for durable transactions on persistent memory. Existing hardware persistent memory transactions overlook an important opportunity for improving performance: some persistent data is algorithmically redundant such that it can be recovered from other data, removing the need for logging such data. The paper presents an ISA extension that enables selective logging for hardware persistent memory transactions for the first time. The ISA extension features two novel components: fine-grain logging and lazy persistency. Fine-grain logging allows hardware to log updates on data in the granularity of words without lengthening the critical path of data accesses. Lazy persistency allows updated data to remain in the cache after the transaction commits. Together, the new hardware persistent memory transaction outperforms the state-of-the-art hardware counterpart by 1.8× on average.

关键词： Computer architecture Maintenance engineering Hardware Optimization IEEE transactions

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Intersecting-boundary-sensitive fingerprinting for tampering detection of DNN models 24

Intersecting-boundary-sensitive fingerprinting for tampering...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Xiaofan Bai Chaoxiang He Xiaojing Ma Bin Benjamin Zhu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security Microsoft School of Computer Science and Technology Huazhong University of Science and Technology and National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab.

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF.

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An Efficient Block Validation Mechanism for UTXO-based Blockchains

An Efficient Block Validation Mechanism for UTXO-based Block...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaohai Dai Bin Xiao Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

关键词： Distributed processing Memory management Prototypes Bitcoin Broadcasting Blockchains Delays

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A nearly optimal distributed algorithm for computing the weighted girth

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Science China(Information Sciences) 2021年第11期64卷 80-94页

作者： Qiang-Sheng HUA Lixiang QIAN Dongxiao YU Xuanhua SHI Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology School of Computer Science and Technology Shandong University

computing the weighted girth, which is the sum of weights of edges in the minimum weight cycle,is an important problem in network analysis. The problem for distributively computing girth in unweighted graphs has garnered lots of attention, but there are few studies in weighted graphs. In this paper, we propose a distributed randomized algorithm for computing the weighted girth in weighted graphs with integral edge weights in the range [1, nc], where n is the number of vertices and c is a constant. The algorithm is devised under the standard synchronous CON GE S T model, which limits each vertex can only transfer O（log n） bits information along each incident edge in a round. The upper bound of the algorithm is O（n log2n） rounds. We also prove the lower bound for computing the weighted girth is ?（D + n/log n） where D is the hop diameter of the weighted graph. This means our distributed algorithm is optimal within a factor of O（log3n）.

关键词： distributed algorithms weighted girth \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

引用

30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback

arXiv

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arXiv 2024年

作者： Bi, Zhangqian Wan, Yao Wang, Zheng Zhang, Hongyu Guan, Batu Lu, Fangxin Zhang, Zili Sui, Yulei Jin, Hai Shi, Xuanhua Huazhong University of Science and Technology China University of Leeds United Kingdom Chongqing University China Shanghai Jiao Tong University China University of New South Wales Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing project-specific information. As much of this project-specific context cannot fit into the prompts of LLMs, we must find ways to allow the model to explore the project-level code context. We present COCOGEN, a new code generation approach that uses compiler feedback to improve the LLM-generated code. COCOGEN first leverages static analysis to identify mismatches between the generated code and the project’s context. It then iteratively aligns and fixes the identified errors using information extracted from the code repository. We integrate COCOGEN with two representative LLMs, i.e., GPT-3.5-Turbo and Code Llama (13B), and apply it to Python code generation. Experimental results show that COCOGEN significantly improves the vanilla LLMs by over 80% in generating code dependent on the project context and consistently outperforms the existing retrieval-based code generation baselines. © 2024, CC BY-NC-SA.

关键词： Static analysis

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SocialEdge: Socialized Learning-Based Request Scheduling for Edge-Cloud Systems

SocialEdge: Socialized Learning-Based Request Scheduling for...

引用

International Conference on Distributed computing Systems

作者： Ziwei Wang Yunfeng Zhao Chao Qiu Qiang He Xin Wang Xiaofei Wang Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and DigitalEconomy (SZ) Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

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Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

arXiv

引用

arXiv 2022年

作者： Xu, Shaoxian Shao, Zhiyuan Yang, Ci Liao, Xiaofei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

The emerging Graph Convolutional Network (GCN) has been widely used in many domains, where it is important to improve the efficiencies of applications by accelerating GCN trainings. Due to the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computations on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set on an input graph, its aggregation stages of backward propagation phases (called as backward aggregations in this paper) can be equivalently converted to partially-active graph processing procedures, which conduct computations on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the graph data used during different training layers of backward aggregations, and constructs their corresponding sub-graphs (called as execution paths in this paper) as inputs to conduct the backward training on GPUs. Further, we propose a structural-aware strategy for the execution paths to compute their optimal group sizes, so as to gain as high as possible performances on GPUs during the backward aggregations. The experiment results by conducting GCN training in typical real-world graphs show that compared with GNNAdvisor, our approach improves the performance of backward aggregations by up to 5.68x on NVIDIA P100 GPU, and up to 6.57x on NVIDIA V100S GPU. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphics processing unit

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