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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Glaze, Nicholas Bayer, Artun Jiang, Xiaoqian Savitz, Sean Segarra, Santiago Rice University Department of Electrical and Computer Engineering HoustonTX United States University of Texas Health Science Center School of Biomedical Informatics HoustonTX United States University of Texas Health Science Center Department of Neurology HoustonTX United States

ISBN: (纸本)9781728163277

Stroke is one of the leading causes of death worldwide, and its mortality rate is drastically higher for patients who suffer recurrent strokes. Motivated by the recent success of graph learning methods on medical tasks, we introduce a graph representation framework for stroke recurrence prediction (GraSReP) based on patient data. In a nutshell, GraSReP sequentially consists of: i) a procedure for converting tabular, time-series patient data to a series of graphs, ii) a graph deep learning architecture that provides embeddings for the patients and, iii) a random forest classifier that predicts the patients' risk of recurrent stroke based on these graph embeddings. We demonstrate GraSReP's effectiveness for predicting recurrent strokes using real-world electronic health records, and discuss how it can be leveraged for the efficient application of preventive care. © 2023 IEEE.

关键词： graph neural network graph representation learning stroke recurrence prediction

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Knowledge structure enhanced graph representation learning model for attentive knowledge tracing

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第3期37卷 2012-2045页

作者： Gan, Wenbin Sun, Yuan Sun, Yi Sokendai Natl Inst Informat Tokyo Japan Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China

Knowledge tracing (KT) is a fundamental personalized-tutoring technique for learners in online learning systems. Recent KT methods employ flexible deep neural network-based models that excel at this task. However, the adequacy of KT is still challenged by the sparseness of the learners' exercise data. To alleviate the sparseness problem, most of the exiting KT studies are performed at the skill-level rather than the question-level, as questions are often numerous and associated with much fewer skills. However, at the skill level, KT neglects the distinctive information related to the questions themselves and their relations. In this case, the models can imprecisely infer the learners' knowledge states and might fail to capture the long-term dependencies in the exercising sequences. In the knowledge domain, skills are naturally linked as a graph (with the edges being the prerequisite relations between pedagogical concepts). We refer to such a graph as a knowledge structure (KS). Incorporating a KS into the KT procedure can potentially resolve both the sparseness and information loss, but this avenue has been underexplored because obtaining the complete KS of a domain is challenging and labor-intensive. In this paper, we propose a novel KS-enhanced graph representation learning model for KT with an attention mechanism (KSGKT). We first explore eight methods that automatically infer the domain KS from learner response data and integrate it into the KT procedure. Leveraging a graph representation learning model, we then obtain the question and skill embeddings from the KS-enhanced graph. To incorporate more distinctive information on the questions, we extract the cognitive question difficulty from the learning history of each learner. We then propose a convolutional representation method that fuses these disctinctive features, thus obtaining a comprehensive representation of each question. These representations are input to the proposed KT model, and the long-term depen

关键词： cognitive question difficulty graph representation learning intelligent tutoring systems knowledge structure discovery knowledge tracing learner proficiency estimation

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Unsupervised graph representation learning Beyond Aggregated View

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 9504-9516页

作者： Zhou, Jian Li, Jiasheng Kuang, Li Gui, Ning Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Unsupervised graph representation learning aims to condense graph information into dense vector embeddings to support various downstream tasks. To achieve this goal, existing UGRL approaches mainly adopt the message-passing mechanism to simultaneously incorporate graph topology and node attribute with an aggregated view. However, recent research points out that this direct aggregation may lead to issues such as over-smoothing and/or topology distortion, as topology and node attribute of totally different semantics. To address this issue, this paper proposes a novel graph Dual-view AutoEncoder framework (GDAE) which introduces the node-wise view for an individual node beyond the traditional aggregated view for aggregation of connected nodes. Specifically, the node-wise view captures the unique characteristics of individual node through a decoupling design, i.e., topology encoding by multi-steps random walk while preserving node-wise individual attribute. Meanwhile, the aggregated view aims to better capture the collective commonality among long-range nodes through an enhanced strategy, i.e., topology masking then attribute aggregation. Extensive experiments on 5 synthetic and 11 real-world benchmark datasets demonstrate that GDAE achieves the best results with up to 49.5% and 21.4% relative improvement in node degree prediction and cut-vertex detection tasks and remains top in node classification and link prediction tasks.

关键词： Topology Encoding Network topology Decoding Data mining Synthetic data Benchmark testing graph representation learning self-supervised topology and attribute features dual-view autoencoder

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GRLC: graph representation learning With Constraints

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第6期35卷 8609-8622页

作者： Peng, Liang Mo, Yujie Xu, Jie Shen, Jialie Shi, Xiaoshuang Li, Xiaoxiao Shen, Heng Tao Zhu, Xiaofeng Univ Elect Sci & Technol China Ctr Future Media Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Technol Chengdu 611731 Peoples R China City Univ London Dept Comp Sci London EC1V 0HB England Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Guangxi Acad Sci Nanning 530007 Peoples R China

Contrastive learning has been successfully applied in unsupervised representation learning. However, the generalization ability of representation learning is limited by the fact that the loss of downstream tasks (e.g., classification) is rarely taken into account while designing contrastive methods. In this article, we propose a new contrastive-based unsupervised graph representation learning (UGRL) framework by 1) maximizing the mutual information (MI) between the semantic information and the structural information of the data and 2) designing three constraints to simultaneously consider the downstream tasks and the representation learning. As a result, our proposed method outputs robust low-dimensional representations. Experimental results on 11 public datasets demonstrate that our proposed method is superior over recent state-of-the-art methods in terms of different downstream tasks. Our code is available at https://***/LarryUESTC/GRLC.

关键词： Task analysis representation learning Semantics Self-supervised learning Training Mutual information Computer science Data mining graph neural networks graph representation learning machine learning

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Propagation Enhanced Neural Message Passing for graph representation learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1952-1964页

作者： Fan, Xiaolong Gong, Maoguo Wu, Yue Qin, A. K. Xie, Yu Xidian Univ Sch Elect Engn Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710126 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Shaanxi Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia Shanxi Univ Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Peoples R China

graph Neural Network (GNN) is capable of applying deep neural networks to graph domains. Recently, Message Passing Neural Networks (MPNNs) have been proposed to generalize several existing graph neural networks into a unified framework. For graph representation learning, MPNNs first generate discriminative node representations using the message passing function and then read from the node representation space to generate a graph representation using the readout function. In this paper, we analyze the representation capacity of the MPNNs for aggregating graph information and observe that the existing approaches ignore the self-loop for graph representation learning, leading to limited representation capacity. To alleviate this issue, we introduce a simple yet effective propagation enhanced extension, Self-Connected Neural Message Passing (SC-NMP), which aggregates the node representations of the current step and the graph representation of the previous step. To further improve the information flow, we also propose a Densely Self-Connected Neural Message Passing (DSC-NMP) that connects each layer to every other layer in a feed-forward fashion. Both proposed architectures are applied at each layer and the graph representation can then be used as input into all subsequent layers. Remarkably, combining these two architectures with existing GNN variants can improve these models' performance for graph representation learning. Extensive experiments on various benchmark datasets strongly demonstrate the effectiveness, leading to superior performance for graph classification and regression tasks.

关键词： Message passing Aggregates Task analysis Data models Predictive models graph neural networks Adaptation models graph data mining graph representation learning graph neural network neural message passing

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A Novel Method for Bitcoin Price Manipulation Identification Based on graph representation learning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 5607-5618页

作者： Zhang, Yanmei Li, Ziyu Su, Yuwen Li, Jianjun Chen, Shiping Cent Univ Finance & Econ Engn Res Ctr State Financial Secur Beijing 102206 Peoples R China Cent Univ Finance & Econ Sch Informat Beijing 102206 Peoples R China Cent Univ Finance & Econ Sch Finance Beijing 102206 Peoples R China CSIRO Data61 Sydney NSW 1710 Australia

Bitcoin is a cryptocurrency designed based on the concept of "decentralization," and its price fluctuation is much larger than those of traditional financial assets, which has raised concerns about intentioned Bitcoin price manipulation. Most of the existing research on Bitcoin price manipulation is limited to the analysis of manipulation behaviors, lacking effective identification methods and performance metrics of identification methods. In this article, we use MtGox real trading data to build a trading network and analyze the diverse price manipulation patterns in the trading network. Based on this, we improve the classical graph representation learning method to effectively identify the price manipulation accounts. Specifically, considering that Bitcoin has special financial investment properties, and its price condenses important information from the financial market, we propose a novel identification algorithm, called Finan2vec, by feeding the time-series information of the price into the transfer strategy of the Node2vec algorithm. This algorithm makes it easier to detect nodes with large trades or significant price-raising behavior. We then use two classification algorithms to complete the identification of abnormal nodes and finally draw on the knowledge of financial market experts to construct a hit rate indicator to measure the effectiveness of the proposed method. The experimental results show that our method can effectively identify price manipulation accounts for Bitcoin and other public blockchain systems.

关键词： Bitcoin representation learning Measurement Security Feature extraction Classification algorithms Behavioral sciences Abnormal accounts bitcoin blockchain graph representation learning price manipulation

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Towards effective urban region-of-interest demand modeling via graph representation learning

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DATA MINING AND KNOWLEDGE DISCOVERY 2024年第6期38卷 3503-3530页

作者： Wang, Pu Sun, Jingya Chen, Wei Zhao, Lei Soochow Univ Sch Comp Sci & Technol 1 Shizhi St Suzhou 215006 Jiangsu Peoples R China Soochow Univ Appl Technol Coll 1 Daxue Rd Suzhou 215006 Jiangsu Peoples R China

Identifying the region's functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due to the diversified and ambiguity nature of urban regions, there are still some significant challenges to be resolved in urban POI demand analysis. To this end, we propose a novel framework, in which Region-of-Interest Demand Modeling is enhanced through the graph representation learning, namely Variational Multi-graph Auto-encoding Fusion, aiming to effectively predict the ROI demand from both the POI level and category level. Specifically, we first divide the urban area into spatially differentiated neighborhood regions, extract the corresponding multi-dimensional natures, and then generate the Spatial-Attributed Region graph (SARG). After that, we introduce an unsupervised multi-graph based variational auto-encoder to map regional profiles of SARG into latent space, and further retrieve the dynamic latent representations through probabilistic sampling and global fusing. Additionally, during the training process, a spatio-temporal constrained Bayesian algorithm is adopted to infer the destination POIs. Finally, extensive experiments are conducted on real-world dataset, which demonstrate our model significantly outperforms state-of-the-art baselines.

关键词： ROI demand graph embedding Activity inference graph representation learning

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A Survey on Malware Detection with graph representation learning

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ACM COMPUTING SURVEYS 2024年第11期56卷 1-36页

作者： Bilot, Tristan El Madhoun, Nour Al Agha, Khaldoun Zouaoui, Anis Iriguard Puteaux La Defense France Univ Paris Saclay CNRS Lab Interdisciplinaire Sci Numer Gif Sur Yvette France ISEP Inst Super Elect Paris LISITE Lab Issy Les Moulineaux France Sorbonne Univ CNRS LIP6 Paris France

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine learning (ML) and notably Deep learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. Recently, the application of graph representation learning (GRL) techniques on graph-structured data has demonstrated impressive capabilities in malware detection. This success benefits notably from the robust structure of graphs, which are challenging for attackers to alter, and their intrinsic explainability capabilities. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures such as Function Call graphs (FCGs) and Control Flow graphs (CFGs). This study also discusses the robustness of GRL-based methods to adversarial attacks, contrasts their effectiveness with other ML/DL approaches, and outlines future research for practical deployment.

关键词： Deep learning DL GNN graph neural networks graph representation learning machine learning malware malware detection ML

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A Survey on graph representation learning Methods

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2024年第1期15卷 1-55页

作者： Khoshraftar, Shima An, Aijun York Univ Elect Engn & Comp Sci Dept Keele St Toronto ON Canada

graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate graph representation vectors that capture the structure and features of large graphs accurately. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification, link prediction and anomaly detection. Many techniques have been proposed for generating effective graph representation vectors, which generally fall into two categories: traditional graph embedding methods and graph neural network (GNN)-based methods. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, whereas a dynamic graph evolves over time and its nodes and edges can be added or deleted from the graph. In this survey, we review the graph-embedding methods in both traditional and GNN-based categories for both static and dynamic graphs and include the recent papers published until the time of submission. In addition, we summarize a number of limitations of GNNs and the proposed solutions to these limitations. Such a summary has not been provided in previous surveys. Finally, we explore some open and ongoing research directions for future work.

关键词： graphs graph representation learning graph neural network graph embedding

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graphPMU: Event Clustering via graph representation learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

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IEEE TRANSACTIONS ON SMART GRID 2023年第4期14卷 2960-2972页

作者： Aligholian, Armin Mohsenian-Rad, Hamed Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs;b) limited prior knowledge about the event signatures due to the fact that the events are diverse and infrequent, and have unknown characteristics. To tackle these challenges, we propose an unsupervised graph-representation learning method, called graphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network;2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that graphPMU can highly outperform the prevalent methods in the literature.

关键词： D-PMU H-PMU locationally scarce measurements feeder topology graph representation learning event clustering

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