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IEEE International Conference on Communications (IEEE ICC)

作者： Liu, Yueliang You, Kangyong Guo, Wenbin Peng, Tao Wang, Wenbo Beijing Univ Posts & Telecommun Wireless Signal Proc & Network Lab Beijing 100876 Peoples R China Sci & Technol Informat Transmiss & Disseminat Com Shijiazhuang 050000 Hebei Peoples R China

ISBN: (纸本)9781538680889

We address the problem of learning hidden graph structure from spatiotemporal signals which are prevalent in distributed sensor networks. Based on a space-time representation model that takes into account correlated properties in dynamic evolution, we formulate the graph learning problem as a regularized multi-convex optimization problem. A correlation-aware and differential smoothness-based graph learning method (CADS) is proposed, which simultaneously estimates the time correlation of each vertex and refines the graph under the differential smoothness prior. The proposed method promotes such smoothness property in each learning step, leading to an improvement of learning accuracy. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed CADS which outperforms the state-of-the-art graph learning methods in classification and prediction tasks.

关键词： graph learning graph signal processing space-time representation correlation differential smoothness

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Deep graph learning for spatially-varying indoor lighting prediction

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Science China(Information Sciences) 2023年第3期66卷 173-187页

作者： Jiayang BAI Jie GUO Chenchen WANG Zhenyu CHEN Zhen HE Shan YANG Piaopiao YU Yan ZHANG Yanwen GUO State Key Lab for Novel Software Technology Nanjing University

Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality(AR) applications in which shading and shadow consistency between virtual and real objects should be guaranteed. However, this is a notoriously ill-posed problem, especially for indoor scenarios,because of the complexity of indoor luminaires and the limited information involved in 2D images. In this paper, we propose a graph learning-based framework for indoor lighting estimation. The core is a new lighting model(DSGLight) based on depth-augmented spherical Gaussians(SGs) and a graph convolutional network(GCN) that infers the new lighting representation from a single low dynamic range(LDR) image of limited field-of-view. Our lighting model builds 128 evenly distributed SGs over the indoor panorama, where each SG encodes the lighting and the depth around that node. The proposed GCN then learns the mapping from the input image to DSGLight. Compared with existing lighting models, our DSGLight encodes both direct lighting and indirect environmental lighting more faithfully and compactly. It also makes network training and inference more stable. The estimated depth distribution enables temporally stable shading and shadows under spatially-varying lighting. Through thorough experiments, we show that our method obviously outperforms existing methods both qualitatively and quantitatively.

关键词： lighting graph learning augmented reality spherical Gaussians rendering

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graph learning UNDER SPARSITY PRIORS

GRAPH LEARNING UNDER SPARSITY PRIORS

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maretic, Hermina Petric Thanou, Dorina Frossard, Pascal Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 Lausanne Switzerland

ISBN: (纸本)9781509041176

graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred from the mere signal observations. This is exactly the problem that we address in this paper, under the assumption that the graph signals can be represented as a sparse linear combination of a few atoms of a structured graph dictionary. The dictionary is constructed on polynomials of the graph Laplacian, which can sparsely represent a general class of graph signals composed of localized patterns on the graph. We formulate a graph learning problem, whose solution provides an ideal fit between the signal observations and the sparse graph signal model. As the problem is non- convex, we propose to solve it by alternating between a signal sparse coding and a graph update step. We provide experimental results that outline the good graph recovery performance of our method, which generally compares favourably to other recent network inference algorithms.

关键词： graph learning graph signal processing Laplacian matrix sparse signal prior graph dictionary

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graph learning INFORMATION CRITERION 47

GRAPH LEARNING INFORMATION CRITERION

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yamada, Koki Tanaka, Yuichi Tokyo Univ Agr & Technol Dept EECS Tokyo Japan Japan Sci & Technol Agcy PRESTO Saitama Japan

ISBN: (纸本)9781665405409

In this paper, we propose a parameter selection method for graph learning. graph learning, a technique of learning graphs from observations, is required in many applications, e.g., classification, prediction, and clustering. However, there is no established method to determine hyperparameters that control the strength of the regularization reflecting prior knowledge. To resolve the problem, we consider a model selection criterion for the graph learning problem based on Laplacian constrained Gaussian Markov random field. The proposed criterion is the value based on model evidence, which is used for model selection in Bayesian statistics. It can be estimated by averaging the negative log-likelihood over the posterior distribution of a graph learning model. To compute this criterion, we present an efficient sampler of the posterior distribution. In the experiment with random graphs, we demonstrate that the proposed method can select hyperparameters having a good trade-off between F-measure and relative error.

关键词： graph learning network topology inference model evidence information criterion

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Auto-weighted multi-view clustering via kernelized graph learning

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PATTERN RECOGNITION 2019年 88卷 174-184页

作者： Huang, Shudong Kang, Zhao Tsang, Ivor W. Xu, Zenglin Univ Elect Sci & Technol China Sch Comp Sci & Engn SMILE Lab Chengdu 611731 Sichuan Peoples R China Univ Technol Sydney Ctr Artificial Intelligence Sydney NSW 2007 Australia

Datasets are often collected from different resources or comprised of multiple representations (i.e., views). Multi-view clustering aims to analyze the multi-view data in an unsupervised way. Owing to the efficiency of uncovering the hidden structures of data, graph-based approaches have been investigated widely for various multi-view learning tasks. However, similarity measurement in these methods is challenging since the construction of similarity graph is impacted by several factors such as the scale of data, neighborhood size, choice of similarity metric, noise and outliers. Moreover, nonlinear relationships usually exist in real-world datasets, which have not been considered by most existing methods. In order to address these challenges, a novel model which simultaneously performs multi-view clustering task and learns similarity relationships in kernel spaces is proposed in this paper. The target optimal graph can be directly partitioned into exact c connected components if there are c clusters. Furthermore, our model can assign ideal weight for each view automatically without additional parameters as previous methods do. Since the performance is often sensitive to the input kernel matrix, the proposed model is further extended with multiple kernel learning ability. With the proposed joint model, three subtasks including construct the most accurate similarity graph, automatically allocate optimal weight for each view and find the cluster indicator matrix can be simultaneously accomplished. By this joint learning, each sub task can be mutually enhanced. Experimental results on benchmark datasets demonstrate that our model outperforms other state-of-the-art multi-view clustering algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： graph learning Multi-view clustering Multiple kernel learning Auto-weighted strategy

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Cloud-Edge-End Collaborative Caching Based on graph learning for Cyber-Physical Virtual Reality

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5097-5108页

作者： Yang, Junchao Guo, Zhiwei Luo, Jiangtao Shen, Yu Yu, Keping Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing Key Lab Intelligent Percept & BlockChain Chongqing 400067 Peoples R China Chongqing Univ Posts & Telecommun Elect Informat & Networking Res Inst Chongqing 400065 Peoples R China Hosei Univ Grad Sch Sci & Engn Tokyo 184858 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

Virtual reality (VR) is an important landing scenario for 5G and a key enabling technology for the digital economy, and the core challenge it faces is how to ensure real-time interaction and content distribution efficiency. Existing solutions rely on adaptive delivery, overall content edge caching, or asynchronous rendering in the cloud, with drawbacks such as high caching and computing costs and difficulty in ensuring real time. This article is oriented to content generation and synchronization of virtual reality, and aims at realizing intelligent transmission of VR content with high efficiency and low delay guarantee, and carries out research on cloud-edge-end collaborative mechanisms. First, for AI-aided VR content generation, a cloud-edge-end collaborative service architecture is constructed to support independent encoding and distribution of background and interactive content generation. Second, to address the issue that the MEC node with limited cache space cannot meet the dynamical demands of VR user, a cloud-edge collaborative caching strategy based on graph neural networks is proposed to achieve optimal caching and updating of background content, in which the background content is first cached MEC node according to the content request with node sharing scheme, then the content is updated according to a minimal cost update algorithm based on the graph neural networks (GNN)-aided request prediction. Finally, the proposed algorithm is simulated and tested, the simulation results show that the proposed caching and update algorithm achieve better quality of experiment (QoE) and higher cache hit ratio compared with comparison algorithms.

关键词： Collaborative caching cyber-physical systems graph learning virtual reality (VR)

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Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using graph learning

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IEEE SYSTEMS JOURNAL 2023年第3期17卷 4567-4578页

作者： Tang, Junqing Wei, Shufen Li, Duo Li, Xiaowei Peking Univ Sch Urban Planning & Design Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peking Univ Shenzhen Grad Sch Minist Nat Resources China Key Lab Earth Surface Syst & Human Earth Relat Shenzhen 518055 Peoples R China Nottingham Trent Univ Dept Engn Nottingham NG1 4FQ England Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China

The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this article, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, U.K. By developing a two-stage graph deep learning model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error of only 11.18 and a root mean square error of 19.49;most importantly, the overall system resilience is maintained at 70% in the meantime. This article provides an alternative perspective for dealing with the well-known "efficiency-resilience" dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

关键词： Resilience Optimization License plate recognition Estimation Transportation Termination of employment Redundancy Automatic number plate recognition (ANPR) graph learning optimization resilience sensor network

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A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5495-5504页

作者： Zhai, Ming-Yue Guangdong Univ Petrochem Technol Sch Elect Informat Engn Maoming Peoples R China

Recently, there is a potential technology called graph-based signal processing (GSP) that is being used in many applications. GSP has been used successfully in the domains such as signal and image filtering and processing. In the paper, GSP is used as an applicable method to non-intrusive appliance load monitoring (NILM). In NILM, all of power consumption is disaggregated down to every appliance's consumption without hardware. Although there is over 30 years after NILM was proposed, there are still some problems faced by applications of NILM in real scenario if there is no training data. By combination of NILM with GSP concept, such a challenge is tackled with better performance over existing methods. As the first step, we propose a new graph learning algorithm to get a graph suitable for appliance load representation and for the disaggregation algorithm. In the following steps, graph-based signal processing method is used three times, from representation of the data sets of power measurements. Public datasets are used to demonstrate the proposed method's performance and feasibility.

关键词： graph-based signal processing Non-intrusive appliance load monitoring Energy disaggregation graph learning

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A graph learning Based Approach or Identity Inference in DApp Platform Blockchain

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2022年第1期10卷 438-449页

作者： Liu, Xiao Tang, Zaiyang Li, Peng Guo, Song Fan, Xuepeng Zhang, Jinbo Peking Univ Wangxuan Inst Comp Technol Beijing 100871 Peoples R China YeeZ Tech Beijing Peoples R China ASRes Beijing Peoples R China Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Peking Univ Guanghua Sch Management Beijing 100871 Peoples R China

Current cryptocurrencies, such as Bitcoin and Ethereum, enable anonymity by using public keys to represent user accounts. On the other hand, inferring blockchain account types (i.e., miners, smart contracts or exchanges), which are also referred to as blockchain identities, is significant in many scenarios, such as risk assessment and trade regulation. Existing work on blockchain deanonymization mainly focuses on Bitcoin that supports simple transactions of cryptocurrencies. As the popularity of decentralized application (DApp) platform blockchains with Turing-complete smart contracts, represented by Ethereum, identity inference in blockchain faces new challenges because of user diversity and complexity of activities enabled by smart contracts. In this paper, we propose I(2)GL, an identify inference approach based on big graph analytics and learning to address these challenges. Specifically, I(2)GL constructs a transaction graph and aims to infer the identity of nodes using the graph learning technique based on graph Convolutional Networks. Furthermore, a series of enhancement has been proposed by exploiting unique features of blockchain transaction graph. The experimental results on Ethereum transaction records show that I(2)GL significantly outperforms other state-of-the-art methods.

关键词： Blockchain anonymity identity inference graph learning

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Incomplete Multi-View Clustering With Joint Partition and graph learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第1期35卷 589-602页

作者： Li, Lusi Wan, Zhiqiang He, Haibo Univ Rhode Isl Dept Elect & Comp Engn Kingston RI 02881 USA

Incomplete multi-view clustering (IMC) aims to integrate the complementary information from incomplete views to improve clustering performance. Most existing IMC methods try to fill the incomplete views or directly learn a common representation based on matrix factorization or subspace learning. The former may introduce useless and/or even noisy information especially for data with a large missing rate. The latter relies on the initialization and ignores the data structures. To address these issues, we propose a novel Joint Partition and graph (JPG) learning method for IMC. JPG can be formulated by two key components: unified partition space learning and consensus graph learning. The partition space is more robust to noise and the graph learning helps uncover the data structures. Specifically, JPG iteratively constructs local incomplete graph matrices, generates incomplete base partition matrices, stretches them to produce a unified partition matrix, and employs it to learn a consensus graph matrix. For efficiency, JPG adaptively allocates a large weight to the stretched base partition that is close to the unified partition, determines parameters, and imposes a low-rank constraint on graphs. Finally, the clusters can be obtained directly from the consensus graph. Experimental results on several benchmark datasets demonstrate the effectiveness and superiority of JPG over the state-of-the-art baselines.

关键词： Incomplete multi-view clustering partition fusion graph learning

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