Graph Data Research Articles

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

A novel label-aware global graph construction method and spiking-coded graph neural network for intelligent process fault diagnosis

Fault diagnosis plays a crucial role in ensuring the safety and efficiency of industrial processes. However, traditional techniques often face difficulties in handling large-scale data characterized by complex structures and relationships. To efficiently represent industrial data as graphs and develop a low-energy-cost feature extraction model, a novel label-aware global graph construction method and a spiking graph convolutional network (SGCN) are proposed in this study to achieve intelligent process fault diagnosis. The label-aware method enhances graph data representation by capturing intrinsic correlations and global features. The SGCN integrates graph convolutional layers with spiking encoding, enabling effective feature extraction while offering computational efficiency advantages. A weighted loss function is introduced to mitigate data imbalance issues. Experiments on the Tennessee Eastman process, the Three-phase Flow Facility, and the de-propanizer distillation process demonstrate SGCN’s superior performance over baseline models in various fault scenarios, while significantly reducing computational costs. The proposed method offers promising potential for reliable and efficient fault diagnosis in complex real-world industrial environments.

A review of graph neural network applications in mechanics-related domains

Mechanics-related tasks often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular underlying structures. Consequently, recent years have witnessed a surge in complex mechanics-related applications inspired by the advancements of GNNs. Despite this process, there is a notable absence of a systematic review addressing the recent advancement of GNNs in solving mechanics-related tasks. To bridge this gap, this review article aims to provide an in-depth overview of the GNN applications in mechanics-related domains while identifying key challenges and outlining potential future research directions. In this review article, we begin by introducing the fundamental algorithms of GNNs that are widely employed in mechanics-related applications. We provide a concise explanation of their underlying principles to establish a solid understanding that will serve as a basis for exploring the applications of GNNs in mechanics-related domains. The scope of this paper is intended to cover the categorisation of literature into solid mechanics, fluid mechanics, and interdisciplinary mechanics-related domains, providing a comprehensive summary of graph representation methodologies, GNN architectures, and further discussions in their respective subdomains. Additionally, open data and source codes relevant to these applications are summarised for the convenience of future researchers. This article promotes an interdisciplinary integration of GNNs and mechanics and provides a guide for researchers interested in applying GNNs to solve complex mechanics-related tasks.

A unified active learning framework for annotating graph data for regression tasks

In many domains, effectively applying machine learning models requires a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. Active learning strategies are pivotal in addressing these challenges, particularly for diverse data types such as graphs. Although active learning has been extensively explored for node-level classification, its application to graph-level learning, especially for regression tasks, is not well-explored. We develop a unified active learning framework specializing in graph annotating and graph-level learning for regression tasks on both standard and expanded graphs, which are more detailed representations. We begin with graph collection and construction. Then, we construct various graph embeddings (unsupervised and supervised) into a latent space. Given such an embedding, the framework becomes task agnostic and active learning can be performed using any regression method and query strategy suited for regression. Within this framework, we investigate the impact of using different levels of information for active and passive learning, e.g., partially available labels and unlabelled test data. Despite our framework being domain agnostic, we validate it on a real-world application of software performance prediction, where the execution time of the source code is predicted. Thus, the graph is constructed as an intermediate source code representation. We support our methodology with a real-world dataset to underscore the applicability of our approach. Our real-world experiments reveal that satisfactory performance can be achieved by querying labels for only a small subset of all the data. A key finding is that Graph2Vec (an unsupervised embedding approach for graph data) performs the best, but only when all train and test features are used. However, Graph Neural Networks (GNNs) are the most flexible embedding techniques when used for different levels of information with and without label access. In addition, we find that the benefit of active learning increases for larger datasets (more graphs) and when the graphs are more complex, which is arguably when active learning is the most important.

Anchor Space Optimal Transport as a Fast Solution to Multiple Optimal Transport Problems.

In machine learning, optimal transport (OT) theory is extensively utilized to compare probability distributions across various applications, such as graph data represented by node distributions and image data represented by pixel distributions. In practical scenarios, it is often necessary to solve multiple OT problems. Traditionally, these problems are treated independently, with each OT problem being solved sequentially. However, the computational complexity required to solve a single OT problem is already substantial, making the resolution of multiple OT problems even more challenging. Although many applications of fast solutions to OT are based on the premise of a single OT problem with arbitrary distributions, few efforts handle such multiple OT problems with multiple distributions. Therefore, we propose the anchor space OT (ASOT) problem: an approximate OT problem designed for multiple OT problems. This proposal stems from our finding that in many tasks the mass transport tends to be concentrated in a reduced space from the original feature space. By restricting the mass transport to a learned anchor point space, ASOT avoids pairwise instantiations of cost matrices for multiple OT problems and simplifies the problems by canceling insignificant transports. This simplification greatly reduces its computational costs. We then prove the upper bounds of its 1 -Wasserstein distance error between the proposed ASOT and the original OT problem under different conditions. Building upon this accomplishment, we propose three methods to learn anchor spaces for reducing the approximation error. Furthermore, our proposed methods present great advantages for handling distributions of different sizes with GPU parallelization.

Visualising the school: a critical study on co-designing a Moodle-based learning management system

ABSTRACT Critical research on data visualisations in education has so far mostly focused on the representation of numeric data in dashboards or graphs. Consequently, the question of how schools are made visible and (re)enacted through visual structures on data-mediated interfaces more broadly – what we call Graphic Data Visualisations (GDV) – has remained under-researched. Addressing that research gap, this study specifically scrutinises the role of GDV as non-human actors within the design process of a Moodle-based learning management system (LMS) in four schools. Following a critical co-design approach, the potential of GDV to reflect on how the visual design shapes educational practices, and to adapt it according to pedagogical and organisational needs is explored. Focusing on ‘critical incidents’ in the design process, we found that local practices substantially influenced the data-based, visual enactment of a school through specific GDV. At the same time, the use of certain GDV enhanced a general critical engagement with datafication among school actors.

Cybersecurity Threats and Vulnerabilities in Online Banking Systems

The rapid expansion of online banking has introduced significant convenience and accessibility for consumers and financial institutions alike. However, it also brings a substantial increase in cybersecurity threats, making online banking systems prime targets for cybercriminals. This paper provides a comprehensive examination of the prevalent cybersecurity threats that online banking faces, including phishing attacks, malware, ransomware, man-in-the-middle (MITM) attacks, insider threats, and distributed denial-of-service (DDoS) attacks. We analyze these threats in-depth, exploring how each tactic is deployed to compromise security and exploit vulnerabilities within online banking systems. Moreover, this paper discusses specific vulnerabilities that exist in online banking platforms, such as weak authentication practices, insecure network connections, outdated software, and risks associated with third-party integrations. Through tables and graphical data, the paper offers a clear overview of the most common vulnerabilities and their prevalence, providing insights into how these weak points are exploited in the cyber landscape. The impact of such cybersecurity breaches on financial institutions is also considered, highlighting the consequences that follow a security breach, such as financial losses, reputational damage, regulatory fines, and customer distrust. The findings reveal that these impacts not only affect individual financial institutions but can also undermine public confidence in digital banking as a whole. Finally, the paper proposes several strategic defenses against these threats. Solutions include multi-factor authentication, end-to-end encryption, robust threat monitoring, regular security audits, and customer education initiatives, among others. Statistical data on the effectiveness of these strategies demonstrates their role in mitigating cyber risks and fortifying online banking systems against future attacks. This study concludes by emphasizing the critical need for continuous innovation in cybersecurity practices, as cyber threats continue to evolve in sophistication.

GRASNet: A novel graph neural network for improving human action recognition and well-being assessment in smart manufacturing

In the manufacturing environment, prioritizing the well-being of workers is both essential and challenging. Recognizing and monitoring workers’ actions in real time is crucial in ensuring safety and health. This study introduces innovative approaches leveraging smart manufacturing technologies and Graph Neural Networks (GNNs) to enhance human action recognition using accessible skeletal data. Traditional Neural Networks encounter limitations in processing readily accessible human-related data, such as image and skeletal data, prompting the development of the Graph Residual Attention SAGE Network (GRASNet). GRASNet exhibits superior capabilities in capturing intricate local connectivity within graph data, ensuring a delicate balance of complexity and efficiency. Furthermore, experimental validations on two manufacturing-related datasets, derived from the NTU RGB + D 120 benchmark dataset, underscore GRASNet’s ability for high-accuracy monitoring of human abnormal behaviors and health issues, positioning it ahead of other existing deep learning models.

Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.

A Statistical Analysis of Knowledge Graph and its Applications

In today's fast-paced world driven by big data and artificial intelligence, organizing and representing vast amounts of knowledge is crucial. Knowledge graphs are dynamic structures serve as repositories for real-world knowledge, presented in the form of interconnected graph data. With their ability to encapsulate complex information, knowledge graphs have swiftly captured the attention of both academia and industry alike. In this paper, we dive deep into understanding knowledge graphs. In first Section, we start by looking at their origins and evolution. In second section describes knowledge graphs, compares them with other graph forms, and explores how academic societies have incorporated them. In conclusion, we discuss the various applications of knowledge graphs in various domains and highlight the software tools that are facilitating their advancement. By providing a systematic overview of knowledge graphs, we aim to illuminate new avenues for research and foster advancements in this burgeoning field.

GraNDe: Efficient Near-Data Processing Architecture for Graph Neural Networks

Graph Neural Network (GNN) models have attracted attention, given their high accuracy in interpreting graph data. One of the primary building blocks of a GNN model is aggregation, which gathers and averages the feature vectors corresponding to the nodes adjacent to each node. Aggregation works by multiplying the adjacency and feature matrices. The size of both matrices exceeds the on-chip cache capacity for many realistic datasets, and the adjacency matrix is highly sparse. These characteristics lead to little data reuse, causing intensive main-memory accesses during the aggregation process. Thus, aggregation exhibits memory-intensive characteristics and dominates most of the total execution time. In this paper, we propose GraNDe, an NDP architecture that accelerates memory-intensive aggregation operations by locating NDP modules near DRAM datapath to exploit rank-level parallelism. GraNDe maximizes bandwidth utilization by separating the memory channel path with the buffer chip in between so that pre-/post-processing in the host processor and reduction in NDP modules operate simultaneously. By exploring the preferred data mappings of the operand matrices to DRAM ranks, we architect GraNDe to support adaptive matrix mapping that applies the optimal mapping for each layer depending on the dimension of the layer and the configuration of a memory system. We also propose adj-bundle broadcasting and re-tiling optimizations to reduce the transfer time for adjacency matrix data and to improve feature vector data reusability by exploiting tiling with consideration of adjacency between nodes. GraNDe achieves 3.01× and 1.69× on average, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.00\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.98\times$</tex-math></inline-formula> speedups of GCN aggregation over the baseline system and the state-of-the-art NDP architecture for GCN, respectively.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no retraining, which significantly reduces the computation costs. Furthermore, we design a cosparsifying strategy to comprehensively trim all the three core elements of GNNs: graph structures, node features, and model parameters. Next, to refine the pruning operation, we introduce a regrowth process into our CGP framework, to reestablish the pruned but important connections. The proposed CGP is evaluated over a node classification task across six GNN architectures, including shallow models [graph convolutional network (GCN) and graph attention network (GAT)], shallow-but-deep-propagation models [simple graph convolution (SGC) and approximate personalized propagation of neural predictions (APPNP)], and deep models [GCN via initial residual and identity mapping (GCNII) and residual GCN (ResGCN)], on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark (OGB). Experiments reveal that the proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of the existing methods.

Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation.

To facilitate more accurate and explainable recommendation, it is crucial to incorporate side information into user-item interactions. Recently, knowledge graph (KG) has attracted much attention in a variety of domains due to its fruitful facts and abundant relations. However, the expanding scale of real-world data graphs poses severe challenges. In general, most existing KG-based algorithms adopt exhaustively hop-by-hop enumeration strategy to search all the possible relational paths, this manner involves extremely high-cost computations and is not scalable with the increase of hop numbers. To overcome these difficulties, in this article, we propose an end-to-end framework Knowledge-tree-routed UseR-Interest Trajectories Network (KURIT-Net). KURIT-Net employs the user-interest Markov trees (UIMTs) to reconfigure a recommendation-based KG, striking a good balance for routing knowledge between short-distance and long-distance relations between entities. Each tree starts from the preferred items for a user and routes the association reasoning paths along the entities in the KG to provide a human-readable explanation for model prediction. KURIT-Net receives entity and relation trajectory embedding (RTE) and fully reflects potential interests of each user by summarizing all reasoning paths in a KG. Besides, we conduct extensive experiments on six public datasets, our KURIT-Net significantly outperforms state-of-the-art approaches and shows its interpretability in recommendation.

Efficient Task Scheduling for Large-scale Graph Data Processing in Cloud Computing: A Particle Swarm Optimization Approach

To fulfil the requirements of task scheduling for processing massive amounts of graph data in cloud computing environments, this thesis offers the LGPPSO method, which is based on Particle Swarm Optimisation. The LGPPSO algorithm considers the task’s overall structure when initialising numerous particles in order to broaden the search range and raise the likelihood of finding an approximation optimal solution. We evaluated it in large-scale simulation trials with 100 performance-heterogeneous virtual machines (VMs) using both randomly generated and real application graphs, and evaluated its effectiveness against the CCSH and HEFT algorithms. The experimental findings demonstrate that, in both randomly generated graphs and real graph structure applications, significantly reducing the scheduling duration of large-scale graph data is the LGPPSO algorithm. For randomly generated 200 and 400 node tasks, respectively, the scheduling length is shortened by approximately 8.3% and 9.7% on average when compared to the HEFT algorithm. The LGPPSO technique minimises the scheduling length for actual graphical structure applications by an average of 14.6% and 16.9% for the Gaussian 100 and 200 node examples, respectively.

Peningkatan Produktivitas Karyawan PT. NBI melalui Pelatihan Microsoft Excel sebagai Strategi dalam Pengelolaan Data Perusahaan

PT Nusantara Building Industries (NBI) is one of the national private companies located in Central Java Province which is engaged in the building materials industry in the form of symmetrical corrugated cement chrysotile fiber sheets. PT NBI tries to meet the needs of consumers according to demand and make various innovations in producing its products. Therefore, it is necessary to carry out a training to improve the soft skills of PT NBI employees in the form of Microsoft Excel training. Microsoft Excel is a software application used to carry out various activities, including data analysis, graph making, complex calculations, and various other tasks. Based on the purpose of implementing community service activities, namely increasing employee productivity. So that the implementation of this training is in accordance with the objectives, namely being able to assist PT NBI employees in finding effective ways to manage and analyze company data. In addition, it can increase understanding in the use of Microsoft Excel. The focus of Microsoft Excel training on the AND/OR function is used to perform logical evaluation of several conditions or criteria. This training is designed to introduce the use of AND/OR functions in the company. This method aims to increase the effectiveness and efficiency of work when encountering cases in several conditions or criteria. The evaluation results of the training implementation showed a good level of acceptance from PT NBI employees towards understanding and using the AND/OR formula. Keywords — Enterprise, Microsoft Excel, AND/OR Function, Data Analysis.

Finding meaningful paths in heterogeneous graphs with PathWays

Graphs, and notably RDF graphs, are a prominent way of sharing data. As data usage democratizes, users need help figuring out the useful content of a graph dataset. In particular, journalists with whom we collaborate are interested in identifying, in a graph, the connections between entities, e.g., people, organizations, emails, etc. We present a novel method for exploring data graphs through their data paths connecting Named Entities (NEs, in short); each data path leads to a tabular-looking set of results. NEs are extracted from the data through dedicated Information Extraction modules. Our method builds upon the pre-existing ConnectionLens platform and follow-up work in the Abstra project, which builds simple, visual ER-style summaries of semi-structured data. The contribution of the present work, and its novelty, is twofold. First, we propose a novel analysis of entity-to-entity paths contained in datasets of any nature, and propose a new method for ranking paths, leveraging a novel Information Extraction (IE) module we built on top of ChatGPT. Second, we present an efficient approach to enumerate and compute NE paths, based on an algorithm which automatically recommends sub-paths to materialize, and rewrites the path queries using these subpaths. Our experiments demonstrate the interest of NE paths and the efficiency of our method for computing and ranking them.

A Power Grid Topological Error Identification Method Based on Knowledge Graphs and Graph Convolutional Networks

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method’s effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss.

Advancing Lung Ultrasound: The Development and Application of the Anatomical M-Mode Evaluation for Lung Swings (AMLES) Technique in Respiratory Assessment.

Exploring the frontiers of lung ultrasound, this document details a groundbreaking technique for lung function evaluation using anatomical M-mode, also known as angle-independent M-mode. The method, termed AMLES (Anatomical M-Mode Evaluation for Lung Swings), quantifies the displacement of the lung curtain observed at the bilateral costophrenic recesses. This approach translates lung curtain movements into graphical data, offering insights into respiratory mechanics and enhancing the assessment of lung ventilation in emergency settings.

Graph-Based Semi-Supervised Learning with Bipartite Graph for Large-Scale Data and Prediction of Unseen Data

Recently, considerable attention has been directed toward graph-based semi-supervised learning (GSSL) as an effective approach for data labeling. Despite the progress achieved by current methodologies, several limitations persist. Firstly, many studies treat all samples equally in terms of weight and influence, disregarding the potential increased importance of samples near decision boundaries. Secondly, the detection of outlier-labeled data is crucial, as it can significantly impact model performance. Thirdly, existing models often struggle with predicting labels for unseen test data, restricting their utility in practical applications. Lastly, most graph-based algorithms rely on affinity matrices that capture pairwise similarities across all data points, thus limiting their scalability to large-scale databases. In this paper, we propose a novel GSSL algorithm tailored for large-scale databases, leveraging anchor points to mitigate the challenges posed by large affinity matrices. Additionally, our method enhances the influence of nodes near decision boundaries by assigning different weights based on their importance and using a mapping function from feature space to label space. Leveraging this mapping function enables direct label prediction for test samples without requiring iterative learning processes. Experimental evaluations on two extensive datasets (Norb and Covtype) demonstrate that our approach is scalable and outperforms existing GSSL methods in terms of performance metrics.

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.