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A contrastive learning framework of graph reconstruction and hypergraph learning for key node identification

Chaos, Solitons and Fractals 2025年 197卷

作者： Huang, Xu-Dong Zhang, Xian-Jie Zhang, Hai-Feng Data Intelligence Lab Research Center For Data To Cyberspace School of Cyber Science and Technology University of Science and Technology of China Hefei Anhui230026 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei Anhui230601 China

With the emergence of complex networks in various domains, the key node identification has become one of the critical issues that needs to be studied. Traditional index methods typically focus on single structural information, while existing data-driven approaches often rely solely on the intrinsic lower-order features of nodes. However, assessing the importance of nodes requires a comprehensive consideration of both network structures and higher-order features from multiple perspectives. To address these challenges, this paper proposes a novel deep learning framework based on Graph Reconstruction and Hypergraph Contrastive Learning, termed GRHCL. The GRHCL method constructs hypergraph structures from original graphs using random walks, followed by leveraging graph reconstruction and hypergraph learning methods to capture both structural and higher-order embedding features of nodes. Positive and negative node pairs are then constructed across different views for contrastive learning. Finally, the model is trained using a training sample set obtained through a clustering sampling strategy, along with a joint loss function. Comparative experiments against various baseline methods demonstrate that the GRHCL method achieves superior predictive performance with smaller training sets, improving accuracy by over 5% on some datasets compared to the next best-performing method. © 2025 Elsevier Ltd

关键词： Clustering sampling Contrastive learning Graph reconstruction learning Hypergraph learning key node identification

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A Miniaturized Bandwidth Reconfigurable Bandpass Chip Filter with Semi-lumped Topology and GaAs pHEMT Technology

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Chinese Journal of Electronics 2021年第1期30卷 192-198页

作者： LI Linqing ZHU Haoran NING Xingyu LI Zunwen WU Xianliang Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University

A miniaturized reconfigurable bandpass chip filter with semi-lumped topology and Gallium Arsenide pseudomorphic High electron mobility transistor（GaAs pHEMT） technology is proposed. Semi-lumped topology is employed to instead the traditional lumped inductor with microstrip transmission line, which can reduced the size of the tunable filter significantly. Three-order series and shunt resonated bandpass filter is implemented with shorted stubs and metal-insulator-metal capacitors. Two transmission zeros are introduced with the series resonator and the shunted GaAs FET. By tuning the gate bias circuit of the FET, the capacitance of the series resonator is changed and the bandwidth of the filter is adjusted correspondingly. An equivalent circuit model is developed to interpret the mechanism of the proposed filter circuit. A reconfigurable on chip filter sample operated at 10 GHz is fabricated to validate the design. Two fractional bandwidth of 14.3% and 23.5% are tuned with bias voltage of the FET, while insertion loss of 2.4 d B and 2.2 d B are observed with the filter, respectively. The area of the chip filter is 0.86 × 0.96 mm2and is equivalent to an electrical length of 0.08 × 0.09λg2at center frequency. Measurement results agree well with the simulation ones.

关键词： gallium arsenide tunable filter transmission zeros equivalent circuit model resonator filters shunted FET III-V semiconductors miniaturized bandwidth reconfigurable bandpass chip filter MIM devices microstrip transmission line chip filter sample shunt resonated bandpass filter metal-insulator-metal capacitors semilumped topology loss 2.4 dB Gallium Arsenide pseudomorphic High electron mobility transistor technology insertion loss filter circuit integrated circuit design GaAs integrated circuit manufacture frequency 10.0 GHz pHEMT technology microwave filters loss 2.2 dB HEMT integrated circuits series resonator miniaturized reconfigurable bandpass chip filter band-pass filters

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A full-rate cooperative relay transmission scheme with randomized cyclic delay diversity

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Journal of Computational Information Systems 2014年第1期10卷 275-284页

作者： Xu, Lei Bao, Menglin Jiang, Ran Zhang, Hongwei Li, Xiaohui Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

Randomized cyclic delay diversity (RCDD) is an effective means to capture both the space diversity and the frequency diversity with low complexity detection over frequency-selective fading channels. Since many existing full-rate cooperative relay transmission schemes have the disadvantages of low spectrum efficiency and high complexity detection, a practical and low complexity detection full-rate distributed cooperative relay transmission scheme is proposed in this paper by taking advantages of the randomized cyclic delay diversity method and the linear constellation precoding (LCP) technology. The bit error rate (BER) performance of the proposed scheme is superior to that of the system with fixed cyclic delay diversity (FCDD) when the number of subcarriers is sufficiently large. Moreover, the system diversity gain improves with the multipath number of the frequency-selective fading channel and the relay number increases. Copyright © 2014 Binary Information Press.

关键词： Cooperative communication

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A full-rate and low-detection complexity cooperative transmission scheme with cyclic delay diversity

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Journal of Computational Information Systems 2013年第11期9卷 4593-4600页

作者： Xu, Lei Wang, Li Zhang, Hongwei Li, Xiaohui Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

The use of orthogonal channels for the cooperative transmission results in a loss of rate or spectral efficiency, and the exiting full-rate cooperative transmission schemes based on space-time code design have the defect of high detection complexity. Therefore, by adopting the non-orthogonal amplify and forward (NAF) mode, combining with the advantages of linear constellation precoding (LCP) technique and cyclic delay diversity (CDD) technique in this paper, a new full-rate wireless cooperative transmission scheme designed based on space-time-frequency codes is proposed. Compared with the existing full-rate cooperative transmission scheme, this new scheme is suitable for multi-relay scenario, and has the advantages of simple structure, low signal detection complexity which does not increase with the relay number. Compared with the equal power allocation scheme, the optimal power allocation scheme can make the system bit error rate (BER) performance improved. © 2013 by Binary Information Press.

关键词： Spectrum efficiency

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Hierarchical Covering Algorithm

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Tsinghua Science and Technology 2014年第1期19卷 76-81页

作者： Jie Chen Shu Zhao Yanping Zhang Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.

关键词： deep architecture hierarchy fuzzy equivalence relation covering tree MNIST dataset

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MICkNN:Multi-Instance Covering kNN Algorithm

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Tsinghua Science and Technology 2013年第4期18卷 360-368页

作者： Shu Zhao Chen Rui Yanping Zhang Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University

Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a lab.led bag that consists of a number of unlab.led instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not lab.led, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the lab.ls of test bags according to predict the lab.ls of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the lab.l of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we lab.l the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.

关键词： mining ambiguous data multi-instance classification constructive covering algorithm kNN algorithm

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A distributed high-rate and low-detection complexity cooperative relay transmission scheme

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Journal of Information and Computational Science 2014年第18期11卷 6591-6599页

作者： Xu, Lei Jiang, Ran Zhang, Gongquan Zou, Mingquan Li, Xiaohui Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

Since the existing full-rate cooperative transmission schemes have a serious defect in low Bit Error Rate (BER) performance while the existing high-rate cooperative transmission schemes have a serious defect in low spectral efficiency, a distributed high-rate cooperative relay transmission scheme based on full-rate cooperative communication model is proposed in this paper, in which Cyclic Delay Diversity (CDD) technology and Linear Constellation Precoding (LCP) technology are employed. Moreover, the proposed scheme addresses the issue of obtaining maximum spatial and multipath diversity with low decoding complexity and high transmission reliability. The BER performance of the proposed scheme is contrasted to that of the full-rate transmission scheme where better performance is achieved, and is improved when the number of the multipath or the relay nodes increases.

关键词： Bit error rate

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Distributed space-time-frequency cooperative transmission scheme for bidirectional multi-relay communication networks

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Journal of Communications 2016年第10期11卷 896-902页

作者： Xu, Lei Ni, Ying Zhao, Kai Zhang, Hong-Wei Li, Xiao-Hui Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China

Spectral efficiency and transmission reliability are among the main design considerations of modern wireless communication systems. In order to solve the problems of high decoding complexity, low spectrum utilization in bidirectional multi-relay cooperative communication system, combining the advantages of Random Cyclic Delay Diversity (RCDD) technology and Adaptive Forwarding Factor (AFF), a distributed space-time-frequency cooperative transmission scheme is proposed in this paper, which has the advantages of low decoding complexity and high spectrum utilization. Compared with the scheme only using Cyclic Delay Diversity (CDD) technology, this scheme can further excavate the diversity gain of the system and improve the performance of Bit Error Ratio (BER) and channel capacity. © 2016 Journal of Communications.

关键词： Cooperative communication

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A simple distributed cooperative transmission scheme for two-way wireless relay networks

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Journal of Computational Information Systems 2013年第8期9卷 3027-3033页

作者： Zhang, Hongwei Jia, Yingju Gao, Zhiyuan Li, Xiaohui Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

To simplify the design process of coding matrices for data forwarding in two-way wireless relay networks with multiple relay nodes, a distributed cooperative transmission scheme based on cyclic delay diversity (CDD) and conventional space-time codes is proposed in this paper. All relay nodes are divided into several groups firstly. In each group, the relay nodes adopt CDD ways to amplify-and-forward (AF) their received signals and thereby obtain the potential diversity gains. The forwarded signals among different groups are encoded into the form of one full-rate space-time code, which can solve the problem on limited error correction ability in conventional relay schemes only using CDD ways and then further reduce the bit error rate (BER) of the distributed wireless system. Consequently, the design process for two-way relay transmissions is simplified and this method can be extended to other complicated cases. Simulation results verify that the proposed scheme is able to achieve high diversity gains and good bit error performance.

关键词： Space time codes

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Multiple-Instance Learning with Instance Selection via Constructive Covering Algorithm

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Tsinghua Science and Technology 2014年第3期19卷 285-292页

作者： Yanping Zhang Heng Zhang Huazhen Wei Jie Tang Shu Zhao the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University1Hefei 230601 China the Department of Computer Science and Technology Tsinghua University

Multiple-Instance Learning （MIL） is used to predict the unlab.led bags＇ lab.l by learning the lab.led positive training bags and negative training *** bag is made up of several unlab.led instances.A bag is lab.led positive if at least one of its instances is positive,otherwise *** multiple-instance learning methods with instance selection ignore the representative degree of the selected *** example,if an instance has many similar instances with the same lab.l around it,the instance should be more representative than *** on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is *** MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative *** an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training ***,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted *** results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.

关键词： multiple-instance learning instance selection constructive covering algorithm maximal Hausdorff

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