Relational Graph Research Articles

A reduced methane pyrolysis mechanism for above-atmospheric pressure conditions

Methane pyrolysis has recently gained significant attention because of its potential to produce hydrogen without any CO2 emissions. Modeling a methane pyrolysis reactor requires a detailed multi-step reaction mechanism consisting of the full reaction pathway up to soot formation. Several models have been proposed in the literature; however, they have not been validated and calibrated to above-atmospheric pressure conditions. Due to the high number of parameters, adjusting the current models parameter set to high-pressure requires mechanism reduction and optimization. In this work, an existing methane pyrolysis mechanism consisting of 1516 reactions and 325 species was reduced using the directed relation graph with error propagation within a temperature and pressure range of 1000–1400 K and 0.1–4 atm. A skeletal mechanism consisting of 343 reactions and 60 species was obtained; resulting in a reaction and species reduction ratio of 4.4 and 5.3, respectively. The target species concentrations, namely CH4, H2, C2H2, C2H4, C2H6, α-C3H4, p-C3H4, were found to be in good agreement with the original mechanism predictions. Then, the rate parameters of the reduced mechanism were fitted against in-house experimental data in the temperature range of 892–1292 K and at a pressure of 4 atm to extend the model validity to above atmospheric pressures. The optimized model showed significant improvement in capturing the experimental data profiles compared to the reduced model predictions. A carbon element flux transfer was performed to identify the importance of additional pathways under high-pressure in aiding faster methane decomposition. The proposed model can be used for the accurate prediction of methane pyrolysis products at a wide range of temperatures and pressures.

A family of symmetric graphs in relation to 2-point-transitive linear spaces

A family of symmetric graphs in relation to 2-point-transitive linear spaces

Lane extraction from trajectories at road intersections based on Graph Transformer Network

Lane-level road networks are crucial components of high-precision maps and play a significant role in intelligent transportation systems. Extracting lane-level road networks at intersections presents considerable challenges due to the complex structures of intersections and diverse driving behaviors. A graph-learning based method is proposed for extracting lanes from high-precision trajectories at road intersections. A trajectory relation graph is designed to encode the directional, shape, and distance features of trajectories, capturing both the intrinsic and extrinsic relationships between trajectories. Subsequently, a Graph Transformer Network is developed to extract a representative subset of trajectories as lanes. To alleviate the problem of generating missing and extraneous lanes, a set-based lane extraction loss is introduced to achieve implicit pruning of redundancy through the attention mechanism. Comprehensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods in three positional and topological accuracy metrics. The method achieves lane extraction with minimal omissions and redundancies and exhibits strong performance in complex scenarios such as U-turns, lane merging, and lane diverging regions.

Granular concept-enhanced relational graph convolution networks for link prediction in knowledge graph

Link prediction is a task of completing absent triplets by leveraging existing triplets in KG and the ultimate goal is to mitigate the incompleteness and sparsity of KG in terms of content. As well known, Relational Graph Convolutional Networks (R-GCN) model is a promising method for link prediction due to its capability of the graph structure. However, R-GCN mainly relies on information from adjacent nodes, leading to shortcomings in the model for capturing a wider range of relational information. Meanwhile, Formal Concept Analysis (FCA) is increasingly being applied in various fields as an effective data analysis tool. Inspired by this, we integrate FCA into R-GCN to address the shortcomings of R-GCN mentioned above. Specifically, a formal context is first created according to the entities and relations, and granular concepts can be obtained by the formal context. Then the weights of the relational parameters in R-GCN are redistributed based on similarity of granular concepts. Further we develop granular concept-enhanced relational graph convolution networks (GCR-GCN) model, where granular concept can simulate the process of data conceptualization in human brain very well, so it has stronger interpretability compared to the black-box characteristic of R-GCN. Finally, experimental results demonstrate that the GCR-GCN model improves the effectiveness of link prediction by effectively assigning different weights to entities with the same relation. In addition, the granular concept effectively improves the computational efficiency of the model and reduces the storage pressure of the model during computation.

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BackgroundThe integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.ResultsWe propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.ConclusionsExperimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

Time-lagged relation graph neural network for multivariate time series forecasting

Recently, Graph Neural Network-based approaches (GNNs) have been widely studied in Multivariate Time Series (MTS) prediction, which could extract information from the closely related variables for prediction. The variables contained in MTS data are lagged correlated, and the future trends of the lagging variables are guided by the leading variables. However, as the existing approaches only focus on delay-free relations, they cannot utilize the guidance information in leading variables to achieve accurate prediction. To address this issue, we propose a novel frame called the Time-Lagged Relation Graph Neural Network (TLGNN) including two key components: the time-lagged relation graph and the time-lagged relation graph learning. The time-lagged relation graph could explicitly model the time-delay relations among MTS variables by connecting variable nodes at lag intervals. The graph learning module could adaptively extract the time-delay relations among MTS variables. Based on the novel designed graph structure, the TLGNN could extract the guidance information from previous values of leading variables to generate more efficient feature representations for prediction. In experiments, the prediction accuracy is significantly improved due to the full exploration of the time-delay relations. Compared with existing methods, the TLGNN achieves the best results in both the single-step prediction and the multi-step prediction tasks.

Developing a high-fidelity reduced chemical kinetic mechanism for liquefied petroleum gas (LPG)

The current study aimed at developing an optimal and highly accurate reduced chemical kinetic mechanism for the Liquid Petroleum Gas (LPG). A three-stage reduction process including pre-processing, the main body reduction, and post-processing was utilized. In the main body reduction, a five-step reduction schematic was applied to the detailed mechanism including 980 species and 4972 reactions released by LLNL utilizing the Direct Relation Graph Error Propagation (DRGEP) method coupled with the isomer lumping and sensitivity analysis. Four different reduced mechanisms including 88, 73, 67, and 44 species were developed and compared with the experimental data. The laminar flame speed/equivalence ratio, ignition delay/temperature curves of the LPG combustion as well as 3D combustion parameters including in-cylinder pressure, mean temperature, heat release rate, and fuel consumption rate for an LPG-fueled opposed-piston engine were considered. It was seen that the 73 species mechanism results for the ignition delay/temperature and laminar flame speed/equivalence ratio matched with the experimental data with discrepancies of 3% and 5%, respectively. The 88 species and 73 species mechanisms showed good agreement in 3D modeling for all the considered parameters in comparison with the detailed mechanism with error values less than 2%. However, the 67 and 44 mechanisms results were not in accordance with the experimental data. Consequently, the developed 73-species mechanism was considered the optimal mechanism for LPG fuel.

A robust self-training algorithm based on relative node graph

A robust self-training algorithm based on relative node graph

ERST: A Signaled Graph Theory of Discourse Relations and Organization

Abstract In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory, the Penn Discourse Treebank, and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search, and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics, and applications for data in our framework.

Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions.

Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals when reacting with coformers, as a potential and attractive route for drug substance development. However, screening and selecting suitable and appropriate coformers that may potentially react with APIs to successfully form cocrystals is a time-consuming, inefficient, economically expensive, and labour-intensive task. In this study, we implemented GNNs to predict the formation of cocrystals using our introduced API-coformers relational graph data. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting, and artificial neural networks). All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60 and 95. 95% for GCN, GraphSAGE, and RGCN respectively). RGCN demonstrated effectiveness and prevailed among the built graph-based models due to its capability to capture intricate and learn nuanced relationships between entities such as non-ionic and non-covalent interactions or link information between APIs and coformers which are crucial for accurate predictions and representations. These capabilities allows the model to adeptly learn the topological structure inherent in the graph data.

Tactile-GAT: tactile graph attention networks for robot tactile perception classification

As one of the most important senses in human beings, touch can also help robots better perceive and adapt to complex environmental information, improving their autonomous decision-making and execution capabilities. Compared to other perception methods, tactile perception needs to handle multi-channel tactile signals simultaneously, such as pressure, bending, temperature, and humidity. However, directly transferring deep learning algorithms that work well on temporal signals to tactile signal tasks does not effectively utilize the physical spatial connectivity information of tactile sensors. In this paper, we propose a tactile perception framework based on graph attention networks, which incorporates explicit and latent relation graphs. This framework can effectively utilize the structural information between different tactile signal channels. We constructed a tactile glove and collected a dataset of pressure and bending tactile signals during grasping and holding objects, and our method achieved 89.58% accuracy in object tactile signal classification. Compared to existing time-series signal classification algorithms, our graph-based tactile perception algorithm can better utilize and learn sensor spatial information, making it more suitable for processing multi-channel tactile data. Our method can serve as a general strategy to improve a robot’s tactile perception capabilities.

Integrated Extraction of Entities and Relations via Attentive Graph Convolutional Networks

For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency between name entity recognition and relation extraction but also may result in the cumulative propagation of errors. To solve this problem, it is proposed that an end-to-end joint entity and relation extraction model based on the Attention mechanism and Graph Convolutional Network (GCN) to simultaneously extract named entities and their relationships. The model includes three parts: the detection of entity span, the construction of an entity relation weighted graph, and the inference of entity relation type. Firstly, the detection of entity spans is viewed as a sequence labeling problem, and a multi-feature fusion approach for word embedding representation is designed to calculate all entity spans in a sentence to form an entity span matrix. Secondly, the entity span matrix is employed in the Multi-Head Attention mechanism for constructing the weighted adjacency matrix of the entity relation graph. Finally, for the inference of entity relation type, considering the interaction between entities and relations, the entity span matrix and relation connection matrix are simultaneously fed into the GCN for integrated extraction of entities and relations. Our model is evaluated on the public NYT dataset, attaining a precision of 66.4%, a recall of 63.1%, and an F1 score of 64.7% for joint entity and relation extraction, significantly outperforming other approaches. Experiments demonstrate that the proposed model is helpful for inferring entities and relations, considering the interaction between entities and relations through the Attention mechanism and GCN.

Research on the response of different flight modes under concave and convex terrain by Semi-Airborne Transient Electromagnetic Method (SATEM)

With the development and utilization of various mineral resources in horizontal terrain areas gradually becoming saturated, the study of transient electromagnetic method has gradually shifted from flat terrain areas to complex fluctuating and high altitude areas. Due to the characteristics of transmitting source on the ground and receiving point in the air, the Semi-Airborne transient electromagnetic method (SATEM) has advantages such as superior working efficiency, large exploration depth and higher signal-to-noise ratio of collected signals. At present, there are few researches on the influence of the flight mode of the UAV on the collected data. In this paper, the concave terrain and convex terrain are described by using unstructured tetrahedral mesh, and the response of terrain model is calculated. Then the terrain response under different flight modes has been researched and analyzed systematically. The corresponding relative error graph is obtained and compared to summarize the law. Through simulation with different influence parameters, it is found that when the flight mode of the UAV is the same altitude, the SATEM response is less affected by the fluctuating terrain, and when the flight mode of the UAV is along the terrain, the SATEM response is more affected by the fluctuating terrain. The study of the response of UAV to the undulating terrain under different flight modes can provide a reference for the processing and interpretation of SATEM data in the undulating terrain region.

Learning Heterogeneous Relation Graph and Value Regularization Policy for Visual Navigation.

The goal of visual navigation is steering an agent to find a given target object with current observation. It is crucial to learn an informative visual representation and robust navigation policy in this task. Aiming to promote these two parts, we propose three complementary techniques, heterogeneous relation graph (HRG), a value regularized navigation policy (VRP), and gradient-based meta learning (ML). HRG integrates object relationships, including object semantic closeness and spatial directions, e.g., a knife is usually co-occurrence with bowl semantically or located at the left of the fork spatially. It improves visual representation learning. Both VRP and gradient-based ML improve robust navigation policy, regulating this process of the agent to escape from the deadlock states such as being stuck or looping. Specifically, gradient-based ML is a type of supervision method used in policy network training, which eliminates the gap between the seen and unseen environment distributions. In this process, VRP maximizes the transformation of the mutual information between visual observation and navigation policy, thus improving more informed navigation decisions. Our framework shows superior performance over the current state-of-the-art (SOTA) in terms of success rate and success weighted by length (SPL). Our HRG outperforms the Visual Genome knowledge graph on cross-scene generalization with ≈ 56% and ≈ 39% improvement on Hits@ 5* (proportion of correct entities ranked in top 5) and MRR * (mean reciprocal rank), respectively. Our code and HRG datasets will be made publicly available in the scientific community.

Large eddy simulation of soot formation in a turbulent lifted flame with a discretized population balance and a reduced kinetic mechanism

This article presents simulations of a turbulent lifted flame using the large eddy simulation-transport probability density function-discretized population balance equation approach. This approach takes into account the interaction between turbulent reacting flow and soot particle formation. A reduced chemical kinetics mechanism including a series of polycyclic aromatic hydrocarbons (PAHs) species linked to soot formation is generated employing the approach of the directed relation graph error propagation and is tested on a perfectly stirred reactor under varying equivalent ratio conditions and premixed flames. The soot kinetics model includes the PAH-based nucleation and surface condensation, the hydrogen abstraction acetylene addition surface growth and oxidation mechanism, and the size-dependent aggregation. The soot morphology considers the surface area and other geometrical properties for both spherical primary particles and fractal aggregates. The simulation results show, in general, reasonably good agreement with experimental measurements in terms of lifted height, flame shape, flow-field velocity, the hydroxyl radical, and soot volume fraction. A discussion of micromixing and its modeling in the context of the Interaction by Exchange with the Mean model is also presented. To investigate the effect of the soot micromixing frequency factor on soot particles, an additional simulation is conducted where this factor is reduced by a factor of 10 for the soot particles. The maximum soot volume fraction is observed to increase slightly. However, compared with the impact of kinetics on soot modeling, this effect is a minor one.

A framework for generating recommendations based on trust in an informal e-learning environment

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner’s cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model’s robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.

Edge computing task scheduling method based on user’s social relations: a construction and solution for Smart City Library

In the realm of the development of a Smart City Library, the integration of robust edge computing is vital. The research suggests a novel task-scheduling model for edge computing, leveraging user’s social relationships. Analyzing these connections involves constructing a user’s social relationship graph by implementing mathematical convolution and the Jaccard similarity ratio. This precise quantification of social ties ensures secure and reliable task scheduling. An equipment connection graph of a user equipment service is also crafted based on Euclidean distance, aligning task scheduling with device-to-device (D2D) communication conditions. Combining a user’s social relationship graph and a user’s device-service device connection graph creates a task-device bipartite graph. On the other hand, the calculation of a task execution cost and edge weight determination finalize a scheduling model. Implementing the proposed method for constructing a model for edge computing task scheduling based on utilizing the Kuhn–Munkres (KM) algorithm demonstrates positive impacts, which are few delays and less energy consumption, on edge computing task scheduling. For instance, when the social threshold score changes from 02. To 0.6, the total task execution delay time increases from 23 to 32, which is the best when compared with other algorithms. The approach strengthens security and reliability while decreasing task execution delays and energy consumption. This research advances edge computing for Smart City Libraries, promising transformative implications.

Concurrent Access Performance Comparison Between Relational Databases and Graph NoSQL Databases for Complex Algorithms

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

Fully exploring object relation interaction and hidden state attention for video captioning

Video Captioning (VC) is a challenging task of automatically generating natural language sentences for describing video contents. As a video often contains multiple objects, it is comprehensively crucial to identify multiple objects and model relationships between them. Previous models usually adopt Graph Convolutional Networks (GCN) to infer relational information via object nodes, but there exist uncertainty and over-smoothing issues of relational reasoning. To tackle these issues, we propose a Knowledge Graph based Video Captioning Network (KG-VCN) by fully exploring object relation interaction, hidden state and attention enhancement. In encoding stages, we present a Graph and Convolution Hybrid Encoder (GCHE), which uses an object detector to find visual objects with bounding boxes for Knowledge Graph (KG) and Convolutional Neural Network (CNN). To model intrinsic relations between detected objects, we propose a knowledge graph based Object Relation Graph Interaction (ORGI) module. In ORGI, we design triplets (head, relation, tail) to efficiently mine object relations, and create a global node to enable adequate information flow among all graph nodes for avoiding possibly missed relations. To produce accurate and rich captions, we propose a hidden State and Attention Enhanced Decoder (SAED) by integrating hidden states and dynamically updated attention features. Our SAED accepts both relational and visual features, adopts Long Short-Term Memory (LSTM) to produce hidden states, and dynamically update attention features. Unlike existing methods, we concatenate state and attention features to predict next word sequentially. To demonstrate the effectiveness of our model, we conduct experiments on three well-known datasets (MSVD, MSR-VTT, VaTeX), and our model achieves impressive results significantly outperforming existing state-of-the-art models.

Proposal Semantic Relationship Graph Network for Temporal Action Detection

Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this paper, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art results. Code and results are available at http://github.com/Riiick2011/PSRGN .