Graph Construction Research Articles

MAC: Maximal Cliques for 3D Registration.

This paper presents a 3D registration method with maximal cliques (MAC) for 3D point cloud registration (PCR). The key insight is to loosen the previous maximum clique constraint and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each representing a consensus set. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. In addition, we present a variant of MAC if given overlap prior, called MAC-OP. Overlap prior further enhances MAC from many technical aspects, such as graph construction with re-weighted nodes, hypotheses generation from cliques with additional constraints, and hypothesis evaluation with overlap-aware weights. Extensive experiments demonstrate that both MAC and MAC-OP effectively increase registration recall, outperform various state-of-the-art methods, and boost the performance of deep-learned methods. For instance, MAC combined with GeoTransformer achieves a state-of-the-art registration recall of [Formula: see text] on 3DMatch / 3DLoMatch. We perform synthetic experiments on 3DMatch-LIR / 3DLoMatch-LIR, a dataset with extremely low inlier ratios for 3D registration in ultra-challenging cases.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

Risk assessment for autonomous navigation system based on knowledge graph

This paper presents a knowledge graph construction framework based on the Adversarial Interpretive Structure Model (AISM), aiming to reveal the failure interdependencies of autonomous navigation assemblies. The autonomous navigation system is divided into 6 systems with 43 failure modes. The hierarchical correlations between multiple systems are quantified by establishing the distance matrix and adjacency matrix, which are defined as triples to serve the entities and attributes for the knowledge graph. The centrality metrics are applied to evaluate the importance of nodes and to assess the stability of the knowledge graph. The coordination relationships between autonomous navigation systems and the impact associations of failure modes are discussed to validate the reasonability of the proposed framework. The results indicate that The NAVTEX operation panel and The installation base of the temperature sensor are the most essential components in the hierarchical results. The positioning system has a high degree centrality and betweenness centrality, while the server system has the highest closeness centrality. The propagation path from Decrease in positioning accuracy to The installation base of the temperature sensor is investigated, providing the methodological and data basis for decision-making.

DGTNet：dynamic graph attention transformer network for traffic flow forecasting

Abstract Graph-based traffic flow prediction plays a crucial role in urban traffic management and planning. In this paper, we propose a novel Dynamic Graph Attention Transformer Network (DGTnet), which is designed to address the issue of inadequately integrating of temporal and spatial dimensions in traditional models. DGTnet maintains temporal continuity while revealing the complex dynamic relationships between key nodes in the urban traffic system, capturing the periodic changes in the rhythm of city life. Specifically, this study adopts adaptive signal decomposition technology to decompose traffic data into multiple intrinsic mode functions (IMFs), effectively capturing the dynamic changes in traffic flow. This decomposition method is key to the implementation of DGTnet dynamic graph construction, enabling the analysis of traffic flow at different time scales, thereby providing a new perspective for traffic flow prediction research. In the traffic prediction module, we comprehensively consider node, edge, and graph structural information, adopting a multi-head self-attention mechanism to achieve direct cross-modeling in both temporal and spatial dimensions. Finally, we introduce a position-wise feed forward network layer to integrate different types of data and capture nonlinear relationships. The experimental results, based on the public transportation network data sets METR_LA, PEMS_BAY, PEMS03 and PEMS07, demonstrate that DGTNet exhibits notable enhancements in three evaluation indicators, namely the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The pertinent code has been made available for public access at https://github.com/chenjing0616/DGTNet.

Automated Equipment Defect Knowledge Graph Construction for Power Grid Regulation

The normal operation of automated equipment is essential for power grid regulation, making the accurate identification and diagnosis of defects in this equipment highly significant. Constructing a knowledge graph for automated equipment defects offers an effective solution to challenges such as delayed reporting, low efficiency, and data omissions in manually recorded defects. To address this, we developed a framework for constructing an automated equipment defect knowledge graph by designing appropriate patterns and data layers. For knowledge extraction, we introduced two models: RoBERTa-BiLSTM for named entity recognition (NER) and ALBERT-BiGRU for relation extraction (RE), both of which demonstrated improved performance in their respective tasks. Additionally, we applied the KBGAT model for knowledge graph completion. Finally, Neo4j was used for storing, visualizing, and analyzing the knowledge graph, highlighting its significance in the operation of power grids and the advancement of digital power systems.

Graph-Based Methods for Forecasting Realized Covariances

Abstract We forecast the realized covariance matrix of asset returns in the U.S. equity market by exploiting the predictive information of graphs in volatility and correlation. Specifically, we augment the Heterogeneous Autoregressive model via neighborhood aggregation on these graphs. Our proposed method allows for the modeling of interdependence in volatility (also known as spillover effect) and correlation, while maintaining parsimony and interpretability. We explore various graph construction methods, including sector membership and graphical LASSO (for modeling volatility), and line graph (for modeling correlation). The results generally suggest that the augmented model incorporating graph information yields both statistically and economically significant improvements for out-of-sample performance over the traditional models. Such improvements remain significant over horizons up to 1 month ahead, but decay in time. The robustness tests demonstrate that the forecast improvements are obtained consistently over the different out-of-sample sub-periods and are insensitive to measurement errors of volatilities.

StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external data sources beyond their training sets and querying predefined knowledge bases to generate accurate, context-rich responses. Most RAG implementations use vector similarity searches, but the effectiveness of this approach and the representation of knowledge bases remain underexplored. Emerging research suggests knowledge graphs as a promising solution. Therefore, this paper presents StructuGraphRAG, which leverages document structures to inform the extraction process and constructs knowledge graphs to enhance RAG for social science research, specifically using NSDUH datasets. Our method parses document structures to extract entities and relationships, constructing comprehensive and relevant knowledge graphs. Experimental results show that StructuGraphRAG outperforms traditional RAG methods in accuracy, comprehensiveness, and contextual relevance. This approach provides a robust tool for social science researchers, facilitating precise analysis of social determinants of health and justice, and underscores the potential of structured document-informed knowledge graph construction in AI and social science research.

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures

Structure-informed materials informatics is a rapidly evolving discipline of materials science relying on the featurization of atomic structures or configurations to construct vector, voxel, graph, graphlet, and other representations useful for machine learning prediction of properties, fingerprinting, and generative design. This work discusses how current featurizers typically perform redundant calculations and how their efficiency could be improved by considering (1) fundamentals of crystallographic (orbits) equivalency to optimize ordered structures and (2) representation-dependent equivalency to optimize dilute, doped, and defect structures with broken symmetry. It also discusses and contrasts ways of (3) approximating random solid solutions occupying arbitrary lattices under such representations. Efficiency improvements discussed in this work were implemented within ▪ or python toolset for Structure-Informed Property and Feature Engineering with Neural Networks developed by authors since 2019 and shown to increase performance from 2 to 10 times for typical inputs. Throughout this work, the authors explicitly discuss how these advances can be applied to different kinds of similar tools in the community.

Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively.

Design and Performance of a Planetary Gearbox with Two DOFs

The article aims to describe the design and operation of a fundamentally new self-regulating planetary transmission, which, without a control system, changes the gear ratio under the influence of a variable external load. A self-regulating transmission can be created based on a kinematic chain with two degrees of freedom, having only one input. According to the laws of mechanics, such a chain has no definability of motion, since the number of inputs must be equal to the number of degrees of freedom. The equilibrium of a two-movable chain with one input can obtained by creating an additional constraint that substitutes a reaction in the instantaneous center of the intermediate link velocities by the friction moment in the hinge of the intermediate link. The friction moment creates a force constraint, which is taken into account in the equilibrium condition. The obtained equilibrium conditions ensure the definiteness of motion and the ability of self-regulation in the form of an inversely proportional dependence of the speed of the output link on the variable external load. The described method makes it possible to create a fundamentally new class of self-regulating mechanisms in all branches of technology. The interaction of kinematic and force parameters and the construction of parameter graphs was performed using the SolidWorks 2021 program with certain additions. The experimental studies performed confirm the reliability of the theoretical developments.

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

PangeBlocks: customized construction of pangenome graphs via maximal blocks

BackgroundThe construction of a pangenome graph is a fundamental task in pangenomics. A natural theoretical question is how to formalize the computational problem of building an optimal pangenome graph, making explicit the underlying optimization criterion and the set of feasible solutions. Current approaches build a pangenome graph with some heuristics, without assuming some explicit optimization criteria. Thus it is unclear how a specific optimization criterion affects the graph topology and downstream analysis, like read mapping and variant calling.ResultsIn this paper, by leveraging the notion of maximal block in a Multiple Sequence Alignment (MSA), we reframe the pangenome graph construction problem as an exact cover problem on blocks called Minimum Weighted Block Cover (MWBC). Then we propose an Integer Linear Programming (ILP) formulation for the MWBC problem that allows us to study the most natural objective functions for building a graph. We provide an implementation of the ILP approach for solving the MWBC and we evaluate it on SARS-CoV-2 complete genomes, showing how different objective functions lead to pangenome graphs that have different properties, hinting that the specific downstream task can drive the graph construction phase.ConclusionWe show that a customized construction of a pangenome graph based on selecting objective functions has a direct impact on the resulting graphs. In particular, our formalization of the MWBC problem, based on finding an optimal subset of blocks covering an MSA, paves the way to novel practical approaches to graph representations of an MSA where the user can guide the construction.

Multi-view representation learning with dual-label collaborative guidance

Multi-view Representation Learning (MRL) has recently attracted widespread attention because it can integrate information from diverse data sources to achieve better performance. However, existing MRL methods still have two issues: (1) They typically perform various consistency objectives within the feature space, which might discard complementary information contained in each view. (2) Some methods only focus on handling inter-view relationships while ignoring inter-sample relationships that are also valuable for downstream tasks. To address these issues, we propose a novel Multi-view representation learning method with Dual-label Collaborative Guidance (MDCG). Specifically, we fully excavate and utilize valuable semantic and graph information hidden in multi-view data to collaboratively guide the learning process of MRL. By learning consistent semantic labels from distinct views, our method enhances intrinsic connections across views while preserving view-specific information, which contributes to learning the consistent and complementary unified representation. Moreover, we integrate similarity matrices of multiple views to construct graph labels that indicate inter-sample relationships. With the idea of self-supervised contrastive learning, graph structure information implied in graph labels is effectively captured by the unified representation, thus enhancing its discriminability. Extensive experiments on diverse real-world datasets demonstrate the effectiveness and superiority of MDCG compared with nine state-of-the-art methods. Our code will be available at https://github.com/Bin1Chen/MDCG.

Model management to support systems engineering workflows using ontology-based knowledge graphs

System engineering has been shifting from document-centric to model-based approaches, where assets are becoming more and more digital. Although digitisation conveys several benefits, it also brings several concerns (e.g., storage and access) and opportunities. In the context of Cyber-Physical Systems (CPS), we have experts from various domains executing complex workflows and manipulating models in a plethora of different formalisms, each with their own methods, techniques and tools. Storing knowledge on these workflows can reduce considerable effort during system development not only to allow their repeatability and replicability but also to access and reason on data generated by their execution. In this work, we propose a framework to manage modelling artefacts generated from workflow executions. The basic workflow concepts, related formalisms and artefacts are formally defined in an ontology specified in OML (Ontology Modelling Language). This ontology enables the construction of a knowledge graph that contains system engineering data to which we can apply reasoning. We also developed several tools to support system engineering during the design of workflows, their enactment, and artefact storage, considering versioning, querying and reasoning on the stored data. These tools also hide the complexity of manipulating the knowledge graph directly. Finally, we have applied our proposed framework in a real-world system development scenario of a drivetrain smart sensor system. Results show that our proposal not only helped the system engineer with fundamental difficulties like storage and versioning but also reduced the time needed to access relevant information and new knowledge that can be inferred from the knowledge graph.

Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI

Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI

Urban Land Use Classification Model Fusing Multimodal Deep Features

Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs) or topological structure deep features through graph neural networks (GNNs), making it challenging to comprehensively capture the rich semantic information in remote sensing images. To address this limitation, we propose a novel urban land use classification model by integrating both raster and topological structure deep features to enhance the accuracy and robustness of the classification model. First, we divide the urban area into block units based on road network data and further subdivide these units using the fractal network evolution algorithm (FNEA). Next, the K-nearest neighbors (KNN) graph construction method with adaptive fusion coefficients is employed to generate both global and local graphs of the blocks and sub-units. The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. Finally, the transformer is used to fuse both topological and raster structure deep features, and land use classification is completed using the softmax function. Experiments were conducted using high-resolution Google images and Open Street Map (OSM) data, with study areas on the third ring road of Shenyang and the fourth ring road of Chengdu. The results demonstrate that the proposed method improves the overall accuracy and Kappa coefficient by 9.32% and 0.17, respectively, compared to single deep learning models. Incorporating subgraph structure features further enhances the overall accuracy and Kappa by 1.13% and 0.1. The adaptive KNN graph construction method achieves accuracy comparable to that of the empirical threshold method. This study enables accurate large-scale urban land use classification with reduced manual intervention, improving urban planning efficiency. The experimental results verify the effectiveness of the proposed method, particularly in terms of classification accuracy and feature representation completeness.

Rethinking Feature Mining for Light Field Salient Object Detection

Light field salient object detection (LF SOD) has recently received increasing attention. However, most current works typically rely on an individual focal stack backbone for feature extraction. This manner ignores the characteristic of blurred saliency-related regions and contour within focal slices, resulting in insufficient or even inaccurate saliency responses. Aiming at addressing this issue, we rethink the feature mining (i.e., exploration) within focal slices and focus on exploiting informative focal slice features and fully leveraging contour information for accurate LF SOD. First, we observe that the geometric relation between different regions within the focal slices is conducive to useful saliency feature mining if utilized properly. In light of this, we propose an implicit graph learning (IGL) approach. The IGL constructs graph structures to propagate informative geometric relations within the focal slices and all-focus features, and promotes crucial and discriminative focal stack feature mining via graph feature distillation. Second, unlike previous works that rarely utilize contour information, we propose a reciprocal refinement fusion (RRF) strategy. This strategy encourages saliency features and object contour cues to effectively complement each other. Furthermore, a contour hint injection mechanism is introduced to refine the feature expressions. Extensive experiments showcase the superiority of our approach over previous state-of-the-art models with an efficient real-time inference speed. Codes are available at https://github.com/gbliao/IRNet and https://openi.pcl.ac.cn/OpenVision/IRNet .

Arc-flow formulation and branch-and-price-and-cut algorithm for the bin-packing problem with fragile objects

This study introduces an arc-flow formulation and the first branch-and-price-and-cut (BPC) algorithm designed to solve the bin-packing problem with fragile objects (BPPFO). This variant of the bin-packing problem originates in the field of telecommunications, particularly in the allocation of cellular calls to frequency channels. The arc-flow formulation is inspired by previous studies and modifies the graph construction method to accommodate fragility constraints. We proved the correctness of this formulation and demonstrated its superiority in instances with small maximum fragility through extensive experiments. The proposed BPC algorithm leverages advanced cutting and packing techniques and incorporates innovative elements such as problem reduction, additional cutting planes, and a label-setting-based exact pricing algorithm. The experimental results demonstrate that the proposed BPC algorithm is highly competitive with the state-of-the-art algorithm for solving the BPPFO and can successfully solve several previously unsolved instances.

Leveraging recommendations using a multiplex graph database

PurposeBy applying targeted graph algorithms, the method used by the authors enables effective prediction of user interactions and thus fulfils the complex requirements of modern recommender systems. This study sets a new benchmark for multidimensional recommendation strategies and offers a path towards more advanced and user-centric models.Design/methodology/approachTo improve multidimensional data recommendation systems, multiplex graph structures are useful to capture various types of user interactions. This paper presents a novel framework that uses a graph database to compute and manipulate multiplex graphs. The approach enables flexible dimension management and increases expressive power through a specialised algebra designed for multiplex graph manipulation.FindingsThe authors compare the multiplex graph approach with traditional matrix methods, in particular random walk with restart, and show that the method not only provides deeper insights into user preferences by integrating scores from different layers of the multiplex graph, but also outperforming matrix-based approaches in most configurations. The results highlight the potential of multiplex graphs for developing sophisticated and customised recommender systems that significantly improve both performance and explainability.Originality/valueThe study provides a formal specification of a multiplex graph construction based on interaction and content-based information; and the study also developed an algebra dedicated to multiplex graphs, enabling robust and precise graph manipulations necessary for effective recommendation queries. The authors implement these algebraic operations within the Neo4j graph database system with a thorough analysis and experimentation with three different data sets, benchmarked against traditional matrix-based methods.