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Reordering and Compression for Hypergraph Processing

IEEE TRANSACTIONS ON COMPUTERS 2024年第6期73卷 1486-1499页

作者： Liu, Yu Luo, Qi Xiao, Mengbai Yu, Dongxiao Chen, Huashan Cheng, Xiuzhen Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

Hypergraphs are applicable to various domains such as social contagion, online groups, and protein structures due to their effective modeling of multivariate relationships. However, the increasing size of hypergraphs has led to high computation costs, necessitating efficient acceleration strategies. Existing approaches often require consideration of algorithm-specific issues, making them difficult to directly apply to arbitrary hypergraph processing tasks. In this paper, we propose a compression-array acceleration strategy involving hypergraph reordering to improve memory access efficiency, which can be applied to various hypergraph processing tasks without considering the algorithm itself. We introduce a new metric called closeness to optimize the ordering of vertices and hyperedges in the one-dimensional array representation. Moreover, we present an 1/2w-approximation algorithm to obtain the optimal ordering of vertices and hyperedges. We also develop an efficient update mechanism for dynamic hypergraphs. Our extensive experiments demonstrate significant improvements in hypergraph processing performance, reduced cache misses, and reduced memory footprint. Furthermore, our method can be integrated into existing hypergraph processing frameworks, such as Hygra, to enhance their performance.

关键词： Arrays Measurement Heuristic algorithms Proteins partitioning algorithms Optimization Task analysis Graph analytics hypergraph locality reordering optimization

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Evaluating Overfit and Underfit in Models of Network Community Structure

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第9期32卷 1722-1735页

作者： Ghasemian, Amir Hosseinmardi, Homa Clauset, Aaron Univ Colorado Dept Comp Sci Boulder CO 80309 USA Univ Southern Calif Inst Informat Sci Marina Del Rey CA 90292 USA Univ Colorado Boulder CO 80309 USA Univ Colorado Biofrontiers Inst Boulder CO 80305 USA Santa Fe Inst 1399 Hyde Pk Rd Santa Fe NM 87501 USA

A common graph mining task is community detection, which seeks an unsupervised decomposition of a network into groups based on statistical regularities in network connectivity. Although many such algorithms exist, community detection's No Free Lunch theorem implies that no algorithm can be optimal across all inputs. However, little is known in practice about how different algorithms over or underfit to real networks, or how to reliably assess such behavior across algorithms. Here, we present a broad investigation of over and underfitting across 16 state-of-the-art community detection algorithms applied to a novel benchmark corpus of 572 structurally diverse real-world networks. We find that (i) algorithms vary widely in the number and composition of communities they find, given the same input;(ii) algorithms can be clustered into distinct high-level groups based on similarities of their outputs on real-world networks;(iii) algorithmic differences induce wide variation in accuracy on link-based learning tasks;and, (iv) no algorithm is always the best at such tasks across all inputs. Finally, we quantify each algorithm's overall tendency to over or underfit to network data using a theoretically principled diagnostic, and discuss the implications for future advances in community detection.

关键词： Task analysis Clustering algorithms Probabilistic logic Bayes methods Detection algorithms Prediction algorithms partitioning algorithms Community detection model selection overfitting underfitting link prediction link description

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Joint Optimization of Charging Location and Time for Network Lifetime Extension in WRSNs

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第2期6卷 1186-1197页

作者： Nguyen, Thuy Dung Nguyen, Tuyen Nguyen, Thanh Hung Nguyen, Kien Le Nguyen, Phi Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam Chiba Univ Grad Sch Engn Chiba 2638522 Japan

Wireless rechargeable sensor networks (WRSNs) have emerged as a potential solution to solve the challenge of prolonging battery-powered sensor networks' lifetime. In a WRSN, a mobile charger moves around and charges the rechargeable sensors when stopping at charging spots. This paper newly considers a joint optimization of the charging location and the charging time to avoid node failure in WRSNs. We formulate the optimization and propose a solution for that. The proposal includes an algorithm to find a list of potential charging locations and their associated optimal charging time. Moreover, the Q-learning technique is adopted to determine the optimal next charging location among the candidates. We implement the algorithm and conduct experiments to compare it to the related works. The results show that the proposed algorithm outperforms the others in terms of network lifetime. Specifically, the highest performance gap of our proposal to the best algorithm among the others is 8.29 times.

关键词： Optimization Heuristic algorithms Wireless sensor networks Approximation algorithms Proposals partitioning algorithms Mathematical models WRSN Q-learning on-demand charging algorithm target coverage connectivity

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A Coded Compressed Sensing Scheme for Unsourced Multiple Access

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IEEE TRANSACTIONS ON INFORMATION THEORY 2020年第10期66卷 6509-6533页

作者： Amalladinne, Vamsi K. Chamberland, Jean-Francois Narayanan, Krishna R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

This article introduces a novel scheme, termed coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent advances in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-blocks and, subsequently, adds redundancy using a systematic linear block code. Compressed sensing techniques are then employed to recover sub-blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm. The error probability and computational complexity of this access paradigm are characterized. An optimization framework, which exploits the tradeoff between performance and computational complexity, is developed to assign parity-check bits to each sub-block. In addition, two emblematic parity bit allocation strategies are examined and their performances are analyzed in the limit as the number of active users and their corresponding payloads tend to infinity. The number of channel uses needed and the computational complexity associated with these allocation strategies are established for various scaling regimes. Numerical results demonstrate that coded compressed sensing outperforms other existing practical access strategies over a range of operational scenarios.

关键词： Compressed sensing Encoding partitioning algorithms Computational complexity Maximum likelihood decoding Communication forward error correction unsourced multiple-access compressed sensing

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Coalition Formation-Based Sub-Channel Allocation in Full-Duplex-Enabled mmWave IABN With D2D

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第5期32卷 4503-4518页

作者： Ma, Zhongyu Wang, Yajing Wang, Zijun Han, Guangjie Hao, Zhanjun Guo, Qun Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tech Minist Educ Changzhou 213000 Peoples R China Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China

One of the key techniques for future wireless network is full-duplex-enabled millimeter wave integrated access and backhaul network underlaying device-to-device communication, which is a 3GPP-inspired comprehensive paradigm for higher spectral efficiency and lower latency. However, the multi-user interference (MUI) and residual self-interference (RSI) become the major bottleneck before the commercial application of the system. To this end, we investigate the sub-channel allocation problem for this networking paradigm. To maximize the overall achievable rate under the considerations of MUI and RSI, the sub-channel allocation problem is firstly formulated as an integer nonlinear programming problem, which is intractable to search an optimal solution in polynomial time. Secondly, a coalition formation based sub-channel allocation (CFSA) algorithm is proposed, where the final partition of the sub-channel coalition is iteratively formed by the concurrent link players according to the two defined switching criterions. Thirdly, the properties of the proposed CFSA algorithm are analyzed from the perspectives of Nash stability and uniform convergence. Fourthly, the proposed CFSA algorithm is compared with other reference algorithms through abundant simulations, and superiorities including effectiveness, convergence and sub-optimality of the proposed CFSA algorithm are demonstrated through the kernel indicators.

关键词： Device-to-device communication Millimeter wave communication Resource management Full-duplex system Backhaul networks Interference cancellation partitioning algorithms Millimeter wave integrated access and backhaul device-to-device full-duplex sub-channel allocation

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EosDNN: An Efficient Offloading Scheme for DNN Inference Acceleration in Local-Edge-Cloud Collaborative Environments

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2022年第1期6卷 248-264页

作者： Xue, Min Wu, Huaming Li, Ruidong Xu, Minxian Jiao, Pengfei Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Kanazawa Univ Inst Sci & Engn Kanazawa Ishikawa 9201192 Japan Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Tianjin Univ Ctr Biosafety Res & Strategy Tianjin 300072 Peoples R China

With the popularity of mobile devices, intelligent applications, e.g., face recognition, intelligent voice assistant, and gesture recognition, have been widely used in our daily lives. However, due to the lack of computing capacities, it is difficult for mobile devices to support complex Deep Neural Network (DNN) inference. To alleviate the pressure on these devices, traditional methods usually upload part of the DNN model to a cloud server and perform a DNN query after uploading an entire DNN model. To achieve real-time DNN query, we consider the collaboration between local, edge and cloud, and perform DNN query when uploading DNN partitions. In this paper, we propose an Efficient offloading scheme for DNN Inference Acceleration (EosDNN) in a local-edge-cloud collaborative environment, where the DNN inference acceleration is mainly embodied in the optimization of migration delay and realization of real-time DNN query. EosDNN comprehensively considers the migration plan and uploading plan, where for the former, a Particle Swarm Optimization with Genetic Algorithm (PSO-GA) is applied to obtain the distribution of DNN layers under the server with the lowest migration delay, and for the latter, a Layer Merge Uploading Algorithm (LMU) is proposed to obtain DNN partitions and their upload order with efficient DNN query performance. Experimental results demonstrate that EosDNN can be applied to large-scale DNN model migration, which can achieve an ideal migration delay and obtain a more fine-grained DNN partition uploading plan, thereby optimizing DNN query performance.

关键词： Servers Computational modeling Delays Collaboration partitioning algorithms Mobile handsets Genetic algorithms Mobile computing local-edge-cloud collaboration computation offloading DNN inference intelligent applications

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Decision Tree Algorithm Considering Distances Between Classes

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IEEE ACCESS 2022年 10卷 69750-69756页

作者： Lee, Sangyong Lee, Chulhee Mun, Kwon Gi Kim, Dohyun Myongji Univ Dept Ind & Management Engn Yongin 17058 South Korea Calif State Polytech Univ Pomona Technol & Operat Management Pomona CA 91768 USA

Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides a dataset into pure subsets based on impurity measurements such as entropy and Gini. Then relatively "pure" partitions consisting of observations with the (almost) same class are obtained. Gini index is one of the representative indices for measuring the impurity of data. However, the Gini index does not take into account distances between classes. If the distances between classes are considered when measuring impurity, the decision tree algorithm can distinguish clearly observations with different classes. To the end, a new decision tree algorithm based on Rao-Stirling index is proposed considering distances between classes. Rao-Stirling index considers distances between classes in such a way that weights more to pairs of references in more distant classes when measuring data impurity. Experimental results indicate that the proposed method is superior in terms of accuracy, implying that considering the distances between classes can help improve accuracy in DT.

关键词： Impurities Indexes Decision trees Random forests partitioning algorithms Entropy Gain measurement Decision tree distance between classes Rao-Stirling index

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Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis

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IEEE ACCESS 2020年 8卷 199213-199226页

作者： Li, Yanan Peng, Xin Tian, Ying Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Key Lab Modern Opt Syst Shanghai 200093 Peoples R China

In this work, a novel process monitoring method in a block-wised partitioning manor is proposed for plant-wide processes which can be partitioned into several sub-blocks and monitored parallelly. The focus of this method is to reduce the high complexity of global plant-wide process, while to improve the efficiency of local feature extraction. In this method, considering that not all process knowledge is available in the block division process strategy, a novel community discovery (CD) algorithms, based on the similarity of neighbor node weighted Louvain, is introduced into the framework of the multi-block Bayesian inference and principal component analysis (PCA) based plant-wide process monitoring scheme. Firstly, the complex network (CN) theory is used to establish the network topology structure for the global variables of the plant-wide process. Secondly, by analyzing the graph characteristic structure, considering the connection strength between nodes, a more reasonable sub-block division is conducted according to the improved Louvain algorithm. Then, PCA method is used to establish process monitoring model for each sub-block to obtain sub-block monitoring statistics. Finally, the total joint statistics is obtained through Bayesian inference for fault detection. The feasibility and effectiveness, in terms of the detection performance, of this method are demonstrated in a simulated plant-wide process by compared with other state-of-the-art PCA based monitoring methods.

关键词： Principal component analysis Correlation Complex networks Bayes methods partitioning algorithms Plant-wide process monitoring complex networks community discovery algorithms Bayesian inference principal component analysis

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Discovering Transit-Oriented Development Regions of Megacities Using Heterogeneous Urban Data

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2019年第5期6卷 943-955页

作者： Kong, Xiangjie Xia, Feng Ma, Kai Li, Jianxin Yang, Qiuyuan Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

Public transport is of great significance in megacities. Transit-oriented development (TOD) has become a reliable solution to urban sustainable development, which can reshape the urban form and improve its quality. This paper focuses on leveraging heterogeneous mega urban data to answer three critical questions in TOD: what region looks like under TOD concept, which regions have the potential to be TOD regions, and how to construct these TOD regions. For region partition, we propose a connected component-based clustering algorithm, which merges the large amount of public transport stops into representative cluster ones as region centers, and then apply the Voronoi algorithm to locate the region boundaries according to the cluster centers. For TOD region identification, we present a link importance-based random walk method that considers the importance of various transits and further identifies the most valuable regions to be TOD. For discovering functions of TOD regions, we introduce a multifactor-based function characterization approach that combines both the static linguistic factor and human mobility factor together and then derives the actual function distributions. The experiments, which are conducted on three real data sets, show the superiority of the proposed methods to solve the problems of region partition, TOD region identification, and function characterization for the megacities. In the meantime, the results provide support for the government to formulate public policy to construct a TOD city.

关键词： Urban areas partitioning algorithms Clustering algorithms Public transportation Government Indexes Function characterization region identification region partition transit-oriented development (TOD) urban data

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A Ubiquitous Machine Learning Accelerator With Automatic Parallelization on FPGA

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第10期31卷 2346-2359页

作者： Wang, Chao Gong, Lei Li, Xi Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci Hefei 230027 Anhui Peoples R China

Machine learning has been widely applied in various emerging data-intensive applications, and has to be optimized and accelerated by powerful engines to process very large scale data. Recently, the instruction set based accelerators on Field Progarmmable Gate Arrays (FPGAs) have been a promising topic for machine learning applications. The customized instructions can be further scheduled to achieve higher instruction-level parallelism. In this article, we design a ubiquitous accelerator with out-of-order automatic parallelization for large-scale data-intensive applications. The accelerator accommodates four representative applications, including clustering algorithms, deep neural networks, genome sequencing, and collaborative filtering. In order to improve the coarse-grained instruction-level parallelism, the accelerator employs an out-of-order scheduling method to enable parallel dataflow computation. We use Colored Petri Net (CPN) tools to analyze the dependences in the applications, and build a hardware prototype on the real FPGA platform. For cluster applications, the accelerator can support four different algorithms, including K-Means, SLINK, PAM, and DBSCAN. For collaborative filtering applications, it accommodates Tanimoto, euclidean, Cosine, and Pearson Correlation as Similarity metrics. For deep learning applications, we implement hardware accelerators for both training process and inference process. Finally, for genome sequencing, we design a hardware accelerator for the BWA-SW algorithm. Experimental results show that the accelerator architecture can reach up to 25X speedup against Intel processors with affordable hardware cost, insignificant power consumption, and high flexibility.

关键词： Clustering algorithms Hardware Machine learning algorithms Machine learning Out of order partitioning algorithms Parallel processing Machine learning accelerator FPGA out-of-order execution automatic parallelization

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