Transform-based graph topology similarity metrics

被引:5
作者
Drakopoulos, Georgios [1 ]
Kafeza, Eleanna [2 ]
Mylonas, Phivos [1 ]
Iliadis, Lazaros [3 ]
机构
[1] Ionian Univ, Dept Informat, Corfu, Greece
[2] Zayed Univ, Coll Technol & Innovat, Dubai, U Arab Emirates
[3] Democritus Univ Thrace, Lab Math & Informat, Xanthi, Greece
关键词
Graph signal processing; Graph topology; Signal processing kernels; Discrete cosine transform; Tensor algebra; Tensor stack network; Julia; CONVOLUTIONAL NEURAL-NETWORK; IMAGE FUSION; DISCRETE; SIGNAL; JULIA;
D O I
10.1007/s00521-021-06235-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Graph signal processing has recently emerged as a field with applications across a broad spectrum of fields including brain connectivity networks, logistics and supply chains, social media, computational aesthetics, and transportation networks. In this paradigm, signal processing methodologies are applied to the adjacency matrix, seen as a two-dimensional signal. Fundamental operations of this type include graph sampling, the graph Laplace transform, and graph spectrum estimation. In this context, topology similarity metrics allow meaningful and efficient comparisons between pairs of graphs or along evolving graph sequences. In turn, such metrics can be the algorithmic cornerstone of graph clustering schemes. Major advantages of relying on existing signal processing kernels include parallelism, scalability, and numerical stability. This work presents a scheme for training a tensor stack network to estimate the topological correlation coefficient between two graph adjacency matrices compressed with the two-dimensional discrete cosine transform, augmenting thus the indirect decompression with knowledge stored in the network. The results from three benchmark graph sequences are encouraging in terms of mean square error and complexity especially for graph sequences. An additional key point is the independence of the proposed method from the underlying domain semantics. This is primarily achieved by focusing on higher-order structural graph patterns.
引用
收藏
页码:16363 / 16375
页数:13
相关论文
共 50 条
[41]   Image Fusion Based on Region Structure Similarity and Contourlet Transform [J].
Gong, Jiamin ;
Wang, Beibei ;
Deng, Yingna ;
Qiao, Lin ;
Liu, Huabo ;
Xu, Jiachi ;
Zhang, Zhengjun .
2016 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP), 2016, :256-261
[42]   Toward a continuous wavelet transform-based search method for feature selection for classification of spectroscopic data [J].
Ghasemi, Jahan B. ;
Heidari, Z. ;
Jabbari, A. .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2013, 127 :185-194
[43]   Shearlet Transform-Based Novel Method for Multimodality Medical Image Fusion Using Deep Learning [J].
Mergin, Ancy ;
Premi, M. S. Godwin .
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2023, 22 (01)
[44]   Curvelet transform-based identification of void diseases in ballastless track by ground-penetrating radar [J].
Yang, Yong ;
Zhao, Weigang .
STRUCTURAL CONTROL & HEALTH MONITORING, 2019, 26 (04)
[45]   An enhanced technique for online discrete cosine transform-based time-varying delay estimation [J].
Shaltaf, S ;
Abdallah, M .
CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2000, 19 (06) :501-515
[46]   A transform-based method for pricing Asian options under general two-dimensional models [J].
Zhang, Weinan ;
Zeng, Pingping .
QUANTITATIVE FINANCE, 2023, 23 (11) :1677-1697
[47]   Topology -learnable graph convolution for skeleton -based action recognition [J].
Zhu, Guangming ;
Zhang, Liang ;
Li, Hongsheng ;
Shen, Peiyi ;
Shah, Syed Afaq Ali ;
Bennamoun, Mohammed .
PATTERN RECOGNITION LETTERS, 2020, 135 :286-292
[48]   An enhanced technique for online discrete cosine transform-based time-varying delay estimation [J].
Samir Shaltaf ;
Moussa Abdallah .
Circuits, Systems and Signal Processing, 2000, 19 :501-515
[49]   A Compressed Sensing and Porous 9-7 Wavelet Transform-based Image Fusion Algorithm [J].
Li, Qing ;
Shang, Mingsheng .
2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, :4185-4191
[50]   A Novel Surface Electromyographic Gesture Recognition Using Discrete Cosine Transform-Based Attention Network [J].
Nguyen, Phuc Thanh-Thien ;
Kuo, Chung-Hsien .
IEEE SIGNAL PROCESSING LETTERS, 2024, 31 :266-270