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graph machine learning for Improved Imputation of Missing Tropospheric Ozone Data

ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023年第46期57卷 18246-18258页

作者： Betancourt, Clara Li, Cathy W. Y. Kleinert, Felix Schultz, Martin G. Forschungszentrum Julich Julich Supercomp Ctr D-52425 Julich Germany Max Planck Inst Meteorol D-20146 Hamburg Germany

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm "correct and smooth". This algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt - UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

关键词： graph signal processing graph machine learning missing data imputation air quality troposphericozone

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graph machine learning for design of high-octane fuels

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AICHE JOURNAL 2023年第4期69卷 e17971-e17971页

作者： Rittig, Jan G. G. Ritzert, Martin Schweidtmann, Artur M. M. Winkler, Stefanie Weber, Jana M. M. Morsch, Philipp Heufer, Karl Alexander Grohe, Martin Mitsos, Alexander Dahmen, Manuel Rhein Westfal TH Aachen Proc Syst Engn AVT SVT Aachen Germany Aarhus Univ Dept Comp Sci Aarhus Denmark Delft Univ Technol Dept Chem Engn Delft Netherlands Rhein Westfal TH Aachen Chair Comp Sci 7 Aachen Germany Delft Univ Technol Delft Bioinformat Lab Intelligent Syst Delft Netherlands Rhein Westfal TH Aachen Chair High Pressure Gas Dynam Aachen Germany JARA ENERGY Aachen Germany Forschungszentrum Julich Inst Energy & Climate Res IEK Energy Syst Engn 10 D-52425 Julich Germany

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.

关键词： computer-aided molecular design fuel design graph machine learning graph neural networks machine learning optimization renewable fuels spark-ignition engines

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graph machine learning classification using architectural 3D topological models

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2023年第11期99卷 1117-1131页

作者： Alymani, Abdulrahman Jabi, Wassim Corcoran, Padraig Cardiff Univ Welsh Sch Architecture Cardiff CF10 3AT Wales Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine learning (ML) to automatically classify three-dimensional (3D) prototypes with respect to formulating the most appropriate building/ground relationship. Here, ML, a branch of artificial intelligence (AI), can ascertain the most appropriate relationship from a set of examples provided by trained architects. Moreover, the system classifies 3D prototypes of architectural precedent models based on a topological graph instead of 2D images. The system takes advantage of two primary technologies. The first is a software library that enhances the representation of 3D models through non-manifold topology (Topologic). The second is an end-to-end deep graph convolutional neural network (DGCNN). The experimental workflow in this paper consists of two stages. First, a generative simulation system for a 3D prototype of architectural precedents created a large synthetic database of building/ground relationships with numerous topological variations. This geometrical model then underwent conversion into semantically rich topological dual graphs. Second, the prototype architectural graphs were imported to the DGCNN model for graph classification. While using a unique data set prevents direct comparison, our experiments have shown that the proposed workflow achieves highly accurate results that align with DGCNN's performance on benchmark graphs. This research demonstrates the potential of AI to help designers identify the topology of architectural solutions and place them within the most relevant architectural canons.

关键词： machine learning graphs classification deep graph convolutional neural network graph neural network graph machine learning 3D topological models

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graph machine learning Meets Multi-Table Relational Data 24

Graph Machine Learning Meets Multi-Table Relational Data

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Gan, Quan Wang, Minjie Wipf, David Faloutsos, Christos Amazon Shanghai Peoples R China CMU Pittsburgh PA USA Amazon Pittsburgh PA USA

ISBN: (纸本)9798400704901

While graph machine learning, and notably graph neural networks (GNNs), have gained immense traction in recent years, application is predicated on access to a known input graph upon which predictive models can be trained. And indeed, within the most widely-studied public evaluation benchmarks such graphs are provided, with performance comparisons conditioned on curated data explicitly adhering to this graph. However, in real-world industrial applications, the situation is often quite different. Instead of a known graph, data are originally collected and stored across multiple tables in a repository, at times with ambiguous or incomplete relational structure. As such, to leverage the latest GNN architectures it is then up to a skilled data scientist to first manually construct a graph using intuition and domain knowledge, a laborious process that may discourage adoption in the first place. To narrow this gap and broaden the applicability of graph ML, we survey existing tools and strategies that can be combined to address the more fundamental problem of predictive tabular modeling over data native to multiple tables, with no explicit relational structure assumed a priori. This involves tracing a comprehensive path through related table join discovery and fuzzy table joining, column alignment, automated relational database (RDB) construction, extracting graphs from RDBs, graph sampling, and finally, graph-centric trainable predictive architectures. Although efforts to build deployable systems that integrate all of these components while minimizing manual effort remain in their infancy, this survey will nonetheless reduce barriers to entry and help steer the graph ML community towards promising research directions and wider real-world impact.

关键词： graph machine learning graph neural networks relational databases tabular modeling data augmentation

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A systematic mapping study on graph machine learning for static source code analysis

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INFORMATION AND SOFTWARE TECHNOLOGY 2025年 183卷

作者： Maarleveld, Jesse Guo, Jiapan Feitosa, Daniel Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intellig Nijenborgh 9 NL-9747 AG Groningen Netherlands

Context: In recent years, graph machine learning and particularly graph neural networks have seen successful and widespread applications in many fields, including static source code analysis. Such machine learning techniques enable learning on rich information networks capable of representing different relations and entities. However, there have been no comprehensive studies investigating the use of graph machine learning for static source code analysis. There is no complete systematic picture of what techniques may be considered tried and tested, and where opportunities for future improvements can still be found. Objective: The main goal of this study is to provide a broad overview of the state of the art of static source code analysis using graph machine learning. Methods: A systematic mapping was performed covering 4499 studies, presenting a final selection of 323 primary studies. Results: Among the selected studies, seven major sub-domains were identified. The use and combinations of artefacts, different graph representations, different features, and different machine learning models used were collected and categorised. Conclusions: The use of graph learning, and in particular graph neural networks, has increased significantly since 2018. Although a wide variety of methods is used, across every dimension we investigated (artefacts, graphs, features, models), we found small sets of technologies which are used in the vast majority of studies. Future opportunities lie in exploring under-explored domains more thoroughly, exploring the use of additional artefacts alongside source code, and paying more attention to interpretability and explainability.

关键词： graph machine learning graph neural networks Static source code analysis Systematic mapping study

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graph machine learning based Cyber Attack Detection for Mobile Tactical Networks

Graph Machine Learning based Cyber Attack Detection for Mobi...

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41st IEEE Military Communications Conference (IEEE MILCOM) - Communications Supporting Military Operations in a Contested Environment

作者： Nagaraj, Keerthiraj Agnew, Dennis Mangipudi, Pavan K. Starke, Allen Nie, Zixiang McNair, Janise Univ Florida Dept Elect & Comp Engn Gainesville FL 32610 USA Univ S Florida Dept Elect Elect & Commun Engn Tampa FL USA

ISBN: (纸本)9798350321814

First responders and other tactical teams rely on mobile tactical networks to coordinate and accomplish emergent time-critical tasks. The information exchanged through these networks is vulnerable to various strategic cyber network attacks. Detecting and mitigating them is a challenging problem due to the volatile and mobile nature of an ad hoc environment. This paper proposes MalCAD, a graph machine learning-based framework for detecting cyber attacks in mobile tactical software-defined networks. MalCAD operates based on observing connectivity features among various nodes obtained using graph theory, instead of collecting information at each node. The MalCAD framework is based on the XGBOOST classification algorithm and is evaluated for lost versus wasted connectivity and random versus targeted cyber attacks. Results show that, while the initial cyber attacks create a loss of 30%-60% throughput, MalCAD results in a gain of average throughput by 25%-50%, demonstrating successful attack mitigation.

关键词： machine learning cyber attacks graph machine learning intelligence software-defined networking

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Interpolation of missing ozone data using graph machine learning and parameter analysis through eXplainable artificial intelligence comparison

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ENVIRONMENTAL MODELLING & SOFTWARE 2025年 190卷

作者： Jung, Seonje Gil, Junsu Lee, Meehye Betancourt, Clara Schultz, Martin Choi, Yunsoo Joo, Taekyu Kim, Daigon Korea Univ Dept Earth & Environm Sci Seoul 02841 South Korea Korea Univ Inst Basic Sci Seoul 02841 South Korea AXA Konzern AG Colonia Allee 10-20 D-51067 Cologne Germany Julich Supercomp Ctr Julich Res Ctr Wilhelm Johnen Str D-52425 Julich Germany Univ Houston Dept Earth & Atmospher Sci Houston TX 77004 USA Natl Inst Environm Res Div Atmospher Environm Res Incheon South Korea

Ozone (O3), a short-lived climate pollutant, continues to increase despite policies aimed at suppressing its precursors in South Korea. The government operates approximately 500 observatories to monitor O3 and trace gases. Researchers use these data to address the ongoing issue of increasing O3 levels. However, challenges in data retrieval from observatories may introduce biases in O3 studies. In this study, we developed a graph-based machine learning model to simulate missing O3 concentrations for mitigate bias. The model incorporates spatiotemporal distribution characteristics using a merged observation dataset from South Korea in 2021. Regardless of region or length of missing data, the model effectively simulates O3 variations with R2 of up to 0.9 and RMSE of 3.6. To determine the influence of input parameters on O3 interpolation, we used eXplainable AI methods. The results indicated that NO2 is the most important factor in cities, while photochemical indicators are more influential in provinces.

关键词： graph machine learning Missing data interpolation Spatiotemporal ozone characteristics Climate change Surface ozone Explainable machine learning

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A new programmed method for retrofitting heat exchanger networks using graph machine learning

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APPLIED THERMAL ENGINEERING 2024年 242卷

作者： Chen, Xin Huang, JiangBo Zhang, Lin Yangtze Normal Univ Sch Robot Engn Chongqing 408100 Peoples R China

Unsupervised graph machine learning provides a powerful framework for modeling and analyzing heat exchanger networks (HENs). This paper proposes a graph-based thermally guided path search (TGPS) method that systematically identifies and evaluates retrofit options to enhance the thermal performance of HENs. The method represents the HEN as a bipartite graph and uses automated algorithms to search for feasible heat integration paths. Thermodynamically inconsistent paths are filtered out based on temperature feasibility rules. The resulting retrofit options are evaluated using graphical metrics like betweenness centrality and cluster coefficients, as well as thermal performance indicators. A re-routing technique is introduced to address temperature mismatch issues for serial heat exchanger connections. When applied to a Kalina power cycle, the thermal efficiency of the optimum configuration is increased by 9.7%. This method is compared with both pinch analysis and the Energy Transfer Diagram approach, and it is thoroughly tested and verified for an ammonia-water absorption refrigeration cycle as well. This methodology provides an effective tool for retrofitting existing HENs while adhering to thermodynamic principles.

关键词： graph theory graph machine learning Heat exchanger network Ammonia-water absorption refrigeration Ammonia-water power cycle

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G-Learn: A graph machine learning Content Recommendation System for Virtual learning Environments 25th

G-Learn: A Graph Machine Learning Content Recommendation Sys...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Damasceno, Hugo Firmino Rocha, Leonardo Sampaio Serra, Antonio de Barros State Univ Ceara UECE Ctr Sci & Technol CCT Graphs & Computat Intelligence Lab LAGIC Postgrad Program Comp Sci PPGCC Fortaleza Ceara Brazil Inst Technol Networks & Energies IREDE Fortaleza Ceara Brazil

ISBN: (纸本)9783031643118;9783031643125

Recommender Systems in Virtual learning Environments (VLEs) provide personalized suggestions to users based on preferences, interaction history, and behavior. They enhance learning by offering personalized content, increasing engagement, and improving teaching effectiveness. Challenges in VLEs include the cold start problem, data sparsity, and limited coverage. To address these, we propose G-Learn, a recommendation system operating in both supervised and unsupervised models. It utilizes graph machine learning, keyword mining, and similarity techniques to recommend educational materials tailored to each student's performance. We demonstrate G-Learn's effectiveness in a real scenario using data from Homero, a VLE for computer science education developed for the brazilian federal government. Validation shows an average f1-score of 0.64 in unsupervised model and 0.95 in supervised model.

关键词： Recommendation systems Virtual learning environments graph machine learning

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Anti-money laundering powered by graph machine learning: "Show me your friends and I will tell you who you are"

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2023年第1期17卷 243-261页

作者： Astrova, Irina Tallinn Univ Technol Dept Software Sci Appl Artificial Intelligence Grp Akad Tee 21 EE-12618 Tallinn Estonia

Today financial institutions have been investing billions of US dollars to detect money laundering. When financial institutions are found to have their customers conduct money laundering through them, they are subjected to large penalties. Moreover, their reputation suffers greatly through public exposure. In response, financial institutions have been exploring opportunities to use graph machine learning algorithms. This paper describes one of those algorithms called Anti-TrustRank and demonstrates how it can be used to identify money launderers. In contrast to many other algorithms, Anti-TrustRank calls for selecting a very small set of customers to be confirmed by human experts (e.g., compliance officers or analysts) as money launderers. Once this set has been identified, Anti-TrustRank seeks out customers linked (either directly or indirectly) to those money launderers.

关键词： Artificial intelligence machine learning graph machine learning rules machine learning models rules-based systems machine learning systems anti-money laundering (AML) PageRank TrustRank Anti-TrustRank

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