Relational Graph Research Articles

Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism

Predicting future events through temporal knowledge graph reasoning has attracted widespread attention from researchers in recent years. Since future events are unknown, a comprehensive understanding of the semantic correlations and historical evolution patterns of entities and relations is a prerequisite for accurate reasoning. However, current models frequently overlook the semantic correlations of relations and fail to consider that the impact of historical repetitive facts on the query varies over time. To tackle these issues, a novel temporal knowledge graph reasoning model based on Relation Graphs and Time-Guided Attention mechanism (RG-TGA) within a local–global framework was proposed. Specifically, RG-TGA designed a relation graph construction and aggregation method in the local information encoder to capture the structural characteristics of relations. Additionally, an improved relation-aware graph attention network was performed to aggregate the local neighbors of entities. In the global information encoder, RG-TGA adopted a time-guided attention mechanism to allocate attention weights to historical repetitive facts based on their time spans. Finally, temporal knowledge graph reasoning was executed by integrating the local and global information. Experimental results on five public datasets show that RG-TGA outperforms the state-of-the-art model by 2.63%, 3.95%, 3.49%, and 2.71% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

Identifying circRNA-disease association based on relational graph attention network and hypergraph attention network

In recent years, with the in-depth study of circRNA, scholars have begun to discover a synergistic relationship between circRNA and microorganisms. Traditional wet lab experiments in biology require expensive financial, material, and human resources to investigate the relationship between circRNA and diseases. Therefore, we propose a new predictive model for inferring the association between circRNA and diseases, called HAGACDA. Specifically, we first aggregate the unique features of circRNA and diseases themselves through singular value decomposition, Pearson similarity, and the biological information characteristics of circRNA and diseases. Utilizing the competitive relationships between miRNA and other microorganisms, we construct a circRNA-miRNA-disease multi-source heterogeneous network. Subsequently, we use a relational graph attention network to aggregate features based on the structural connections between different nodes. To address the inherent limitations in capturing high-order patterns in edge sets, we integrate a hypergraph attention network to extract features of circRNA and diseases. Finally, association prediction scores for node pairs are obtained through a multilayer perceptron. We conducted a comprehensive analysis of the model, including comparative experiments and case studies. Experimental results demonstrate that our model accurately predicts the association between circRNA and diseases.

Upper ideal relation graphs associated to rings

Upper ideal relation graphs associated to rings

CAGNet: a context-aware graph neural network for detecting social relationships in videos

Social relationships, such as parent-offspring and friends, are crucial and stable connections between individuals, especially at the person level, and are essential for accurately describing the semantics of videos. In this paper, we analogize such a task to scene graph generation, which we call video social relationship graph generation (VSRGG). It involves generating a social relationship graph for each video based on person-level relationships. We propose a context-aware graph neural network (CAGNet) for VSRGG, which effectively generates social relationship graphs through message passing, capturing the context of the video. Specifically, CAGNet detects persons in the video, generates an initial graph via relationship proposal, and extracts facial and body features to describe the detected individuals, as well as temporal features to describe their interactions. Then, CAGNet predicts pairwise relationships between individuals using graph message passing. Additionally, we construct a new dataset, VidSoR, to evaluate VSRGG, which contains 72 h of video with 6276 person instances and 5313 relationship instances of eight relationship types. Extensive experiments show that CAGNet can make accurate predictions with a comparatively high mean recall (mRecall) when using only visual features.

Hyperfiniteness of boundary actions of relatively hyperbolic groups

We show that if G is a finitely generated group hyperbolic relative to a finite collection of subgroups \mathcal{P} , then the natural action of G on the geodesic boundary of the associated relative Cayley graph induces a hyperfinite equivalence relation. As a corollary of this, we obtain that the natural action of G on its Bowditch boundary \partial (G,\mathcal{P}) also induces a hyperfinite equivalence relation. This strengthens a result of Ozawa obtained for \mathcal{P} consisting of amenable subgroups and uses a recent work of Marquis and Sabok.

Interpretable medical image Visual Question Answering via multi-modal relationship graph learning

Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. However, existing medical VQA datasets are small and only contain simple questions (equivalent to classification tasks), which lack semantic reasoning and clinical knowledge. Our previous work proposed a clinical knowledge-driven image difference VQA benchmark using a rule-based approach (Hu et al., 2023). However, given the same breadth of information coverage, the rule-based approach shows an 85% error rate on extracted labels. We trained an LLM method to extract labels with 62% increased accuracy. We also comprehensively evaluated our labels with 2 clinical experts on 100 samples to help us fine-tune the LLM. Based on the trained LLM model, we proposed a large-scale medical VQA dataset, Medical-CXR-VQA, using LLMs focused on chest X-ray images. The questions involved detailed information, such as abnormalities, locations, levels, and types. Based on this dataset, we proposed a novel VQA method by constructing three different relationship graphs: spatial relationships, semantic relationships, and implicit relationship graphs on the image regions, questions, and semantic labels. We leveraged graph attention to learn the logical reasoning paths for different questions. These learned graph VQA reasoning paths can be further used for LLM prompt engineering and chain-of-thought, which are crucial for further fine-tuning and training multi-modal large language models. Moreover, we demonstrate that our approach has the qualities of evidence and faithfulness, which are crucial in the clinical field. The code and the dataset is available at https://github.com/Holipori/Medical-CXR-VQA.

Explicitly Exploiting Implicit User and Item Relations in Graph Convolutional Network (GCN) for Recommendation

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks.

Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis

Portfolio management (PM) is a popular financial process that concerns the occasional reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of maximizing profitability conditioned to a certain level of risk. Given the inherent dynamicity of stock exchanges and development for long-term performance, reinforcement learning (RL) has become a dominating solution for solving the problem of portfolio management in an automated and efficient manner. Nevertheless, the present RL-based PM methods just take into account the variations in prices of portfolio assets and the implications of price variations, while overlooking the significant relationships among different assets in the market, which are extremely valuable for managerial decisions. To close this gap, this paper introduces a novel deep model that combines two subnetworks; one to learn a temporal representation of historical prices using a refined temporal learner, while the other learns the relationships between different stocks in the market using a relation graph learner (RGL). Then, the above learners are integrated into the curriculum RL scheme for formulating the PM as a curriculum Markov Decision Process, in which an adaptive curriculum policy is presented to enable the agent to adaptively minimize risk value and maximize cumulative return. Proof-of-concept experiments are performed on data from three public stock indices (namely S&P500, NYSE, and NASDAQ), and the results demonstrate the efficiency of the proposed framework in improving the portfolio management performance over the competing RL solutions.

A visual knowledge map analysis of coal mine intensity based on CiteSpace software and web of science core database

ABSTRACT The research on coal mining intensity provides a basis for rational coal resource extraction and mine ecological environment source protection. This study uses the 815 publications and based on CiteSpace’s visualisation software, takes the authors, institutions, and keywords as the nodes, and constructs the related knowledge graph through keyword co-occurrence and other methods. China ranks first in the world in this field; the current research mainly focuses on coal and gas protrusion, carbon dioxide emission, etc.; future research on coal mining intensity should focus on high-intensity mining, factors affecting coal mining intensity, and mining disturbance.

A Relational Graph Convolution Network-Based Smart Risk Recognition Model for Financial Transactions

The financial transaction relationships between existing entities are complex and diverse. In this situation, traditional risk control methods mainly ignored such complex and implicit relationship characteristics, remaining difficult to cope with complex and ever-changing financial risks. To address this issue, this paper proposes a novel relational graph convolution network (GCN)-based smart risk recognition model for financial transactions. Firstly, the classic GCN is simplified based on spatiotemporal effect. Then, feature extraction is conducted for financial transaction data, and a transformer encoder-based GCN model is proposed for risk recognition. The proposed model in this work is named as graph transformer graph convolutional network (GT-GCN) for short. In addition, fuzzy evaluation method is added into it. Finally, some experiments are conducted on real-world financial transaction data to make validation for the proposed GT-GCN. The research results indicate that the GT-GCN can not only effectively identify risks in financial transactions, but also has high accuracy and predictive ability. The application of GT-GCN to actual datasets also has good scalability and adaptability, and it can be resiliently extended into many other fields.

Micro-expression recognition based on a novel GCN-transformer cooperation model for IoT-eHealth

If a person is truly healthy, his/her well-being encompasses both physical and psychological health. However, the existing IoT-eHealth system typically focus only on monitoring the user’s physical data through various sensors, neglecting their mental state. To enhance the intelligence level of IoT-eHealth system and enable it to have the psychological monitoring ability, a novel collaborative model based on Graph Convolutional Network (GCN) and Transformer is designed for Micro-Expression (ME) recognition in this paper. Firstly, facial information within each frame is transformed into a Spatial Topological Relationship Graph (STRG) by using facial landmarks detection and psychological relationship of local patches. Then, in order to automatically aggregate the key information on facial patches that contribute to ME recognition from the structured graph data, a Hierarchical Adaptive Graph Pooling (HAGP) module is designed for obtaining discriminative frame-level feature based on GCN utilizing graph structure and vertex global dependencies. Finally, in order to model the long-term dependencies among frames and capture the key frame-level features that are beneficial for ME recognition, a Temporal Sensitive Self-Attention (TSSA) mechanism is designed, and a novel Temporal Sensitive Transformer (TST) encoder is constructed based on TSSA to explore the evolution law of facial patterns and obtain discriminative video-level features that are helpful for ME recognition. In the comparative experiments of standard dataset verification and practical dataset testing, designed collaborative model is superior to other methods and can achieve the highest recognition accuracy, which almost can meet the application requirements of IoT-eHealth system.

Dehazing & Reasoning YOLO: Prior knowledge-guided network for object detection in foggy weather

Fast and accurate object detection in foggy weather is crucial for visual tasks such as autonomous driving and video surveillance. Existing methods typically preprocess images with enhancement techniques before the object detector, so that the real-time performance of object detection decreases to some extent. Meanwhile, many popular object detection models rely solely on visual features for localization and classification. When fog is present, visual features would be so adversely impacted that the detection accuracy sharply decreases. Therefore, we propose an end-to-end prior knowledge-guided network called DR-YOLO for object detection in foggy weather. DR-YOLO integrates the atmospheric scattering model and the co-occurrence relation graph as prior knowledge into the entire training process of the detector. Firstly, Restoration Subnet Module (RSM) is designed to employ the atmospheric scattering model to guide the learning direction of the detector for dehazing features. Specifically, it is only adopted during the training process and does not increase the time cost of detection process. Secondly, for guiding the detector to pay more attention to potential co-occurring objects in the same scene, we introduce Relation Reasoning Attention Module (RRAM) that utilizes the co-occurrence relation graph to supplement deficient visual features in foggy weather. In addition, DR-YOLO employs Adaptive Feature Fusion Module (AFFM) to effectively merge the key features from the backbone and neck for the needs of RRAM and RSM. Finally, we conduct experiments on clear, synthetic and real-world foggy datasets to demonstrate the effectiveness of DR-YOLO. The source code is available at https://github.com/wenxinss/DR-YOLO.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

Modulation instability and optical wave profiles for the conformable Schrödinger–Poisson dynamical system

This manuscript studies the exact solitary wave profiles for the conformable Schrödinger–Poisson dynamical system. This system has a significant role in gravity’s quantum state operation approximates the interaction between quantum mechanics and gravitation. The diverse exact solitary wave profiles are constructed by using the Khater method. The different closed-form solutions are obtained such as dark, singular, hyperbolic, periodic, and plane waves. Moreover, the modulation instability is also discussed for this dynamical system. To, show the physical significance of desired results we plot the 3D and 2D plots along with the related contour graphs, which are also drawn. The constraint criteria for the exact solutions are also exposed.

Learning feature relationships in CNN model via relational embedding convolution layer

Establishing the relationships among hierarchical visual attributes of objects in the visual world is crucial for human cognition. The classic convolution neural network (CNN) can successfully extract hierarchical features but ignore the relationships among features, resulting in shortcomings compared to humans in areas like interpretability and domain generalization. Recently, algorithms have introduced feature relationships by external prior knowledge and special auxiliary modules, which have been proven to bring multiple improvements in many computer vision tasks. However, prior knowledge is often difficult to obtain, and auxiliary modules bring additional consumption of computing and storage resources, which limits the flexibility and practicality of the algorithm. In this paper, we aim to drive the CNN model to learn the relationships among hierarchical deep features without prior knowledge and consumption increasing, while enhancing the fundamental performance of some aspects. Firstly, the task of learning the relationships among hierarchical features in CNN is defined and three key problems related to this task are pointed out, including the quantitative metric of connection intensity, the threshold of useless connections, and the updating strategy of relation graph. Secondly, Relational Embedding Convolution (RE-Conv) layer is proposed for the representation of feature relationships in convolution layer, followed by a scheme called use & disuse strategy which aims to address the three problems of feature relation learning. Finally, the improvements brought by the proposed feature relation learning scheme have been demonstrated through numerous experiments, including interpretability, domain generalization, noise robustness, and inference efficiency. In particular, the proposed scheme outperforms many state-of-the-art methods in the domain generalization community and can be seamlessly integrated with existing methods for further improvement. Meanwhile, it maintains comparable precision to the original CNN model while reducing floating point operations (FLOPs) by approximately 50%.

KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy.

The aim of this project was to create time-aware, individual-level risk score models for adverse drug events related to multiple sclerosis disease-modifying therapy and to provide interpretable explanations for model prediction behavior. We used temporal sequences of observational medical outcomes partnership common data model (OMOP CDM) concepts derived from an electronic health record as model features. Each concept was assigned an embedding representation that was learned from a graph convolution network trained on a knowledge graph (KG) of OMOP concept relationships. Concept embeddings were fed into long short-term memory networks for 1-year adverse event prediction following drug exposure. Finally, we implemented a novel extension of the local interpretable model agnostic explanation (LIME) method, knowledge graph LIME (KG-LIME) to leverage the KG and explain individual predictions of each model. For a set of 4859 patients, we found that our model was effective at predicting 32 out of 56 adverse event types (P < .05) when compared to demographics and past diagnosis as variables. We also assessed discrimination in the form of area under the curve (AUC = 0.77 ± 0.15) and area under the precision-recall curve (AUC-PR = 0.31 ± 0.27) and assessed calibration in the form of Brier score (BS = 0.04 ± 0.04). Additionally, KG-LIME generated interpretable literature-validated lists of relevant medical concepts used for prediction. Many of our risk models demonstrated high calibration and discrimination for adverse event prediction. Furthermore, our novel KG-LIME method was able to utilize the knowledge graph to highlight concepts that were important to prediction. Future work will be required to further explore the temporal window of adverse event occurrence beyond the generic 1-year window used here, particularly for short-term inpatient adverse events and long-term severe adverse events.

Integrating Relational Knowledge With Text Sequences for Script Event Prediction.

Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relational knowledge between events, they regard scripts as sequences or graphs, which cannot capture the relational information between events and the semantic information of script sequences jointly. To address this issue, we propose a new script form, relational event chain, that combines event chains and relational graphs. We also introduce a new model, relational-transformer, to learn embeddings based on this new script form. In particular, we first extract the relationship between events from an event knowledge graph to formalize scripts as relational event chains, then use the relational-transformer to calculate the likelihood of different candidate events, where the model learns event embeddings that encode both semantic and relational knowledge by combining transformers and graph neural networks (GNNs). Experimental results on both one-step inference and multistep inference tasks show that our model can outperform existing baselines, indicating the validity of encoding relational knowledge into event embeddings. The influence of using different model structures and different types of relational knowledge is analyzed as well.

Short-Term Prediction Method of Transient Temperature Field Variation for PMSM in Electric Drive Gearbox Using Spatial-Temporal Relational Graph Convolutional Thermal Neural Network

Short-Term Prediction Method of Transient Temperature Field Variation for PMSM in Electric Drive Gearbox Using Spatial-Temporal Relational Graph Convolutional Thermal Neural Network

Lanthanide metal-organic frameworks as ratiometric fluorescent probes for real-time monitoring of PFOA photocatalytic degradation process

The assessment of perfluorooctanoic acid (PFOA) photocatalytic degradation usually involves tedious pre-treatment and sophisticated instrumentation, making it impractical to evaluate the degradation process in real-time. Herein, we synthesized a series of lanthanide metal-organic frameworks (Ln-MOFs) with outstanding fluorescent sensing properties and applied them as luminescent probes in the photocatalytic degradation reaction of PFOA for real-time evaluation. As the catalytic reaction proceeds, the fluorescence color changes significantly from green to orange-red due to the different interaction mechanisms between the electron-deficient PFOA and smaller radius F− with the ratiometric fluorescent probe MOF-76 (Tb: Eu = 29:1). The limit of detection (LOD) was calculated to be 0.0127 mM for PFOA and 0.00746 mM for F−. In addition, the conversion rate of the catalytic reaction can be read directly based on the chromaticity value by establishing a three-dimensional relationship graph of G/R value-conversion rate-time (G/R indicates the ratio between green and red luminance values in the image.), allowing for real-time and rapid tracking of the PFOA degradation. The recoveries of PFOA and F− in the actual water samples were 99.3–102.7% (RSD = 2.2–4.4%) and 100.7–105.3% (RSD = 3.9–6.8%), respectively. Both theoretical calculations and experiments reveal that the detection mechanism was attributed to the photoinduced electron transfer and energy transfer between the analytes and the probe. This method simplifies the sample analysis process and avoids the use of bulky instruments, and thus has great potential on the design and development of quantitative time-resolved visualization methods to assess catalytic performance and reveal mechanisms.

Molecular Descriptors Of Certain Class Of Carbon Nanocone Networks Through Quotient Graph Approach

Nanoparticles have potential applications in a wide range of fields, including electronics, medicine and material research, because of their remarkable and exceptional attributes. Carbon nanocones are planar carbon networks with mostly hexagonal faces and a few non-hexagonal faces (mostly pentagons) in the core. Two types of nanocone configurations are possible: symmetric and asymmetric, depending on where the pentagons are positioned within the structure. In addition to being a good substitute for carbon nanotubes, carbon nanocones have made an identity for themselves in a number of fields, including biosensing, electrochemical sensing, biofuel cells, supercapacitors, gas storage devices, and biomedical applications. Their astonishing chemical and physical attributes have made them well-known and widely accepted in the fields of condensed matter physics, chemistry, material science, and nanotechnology. Mathematical and chemical breakthroughs were made possible by the concept of modeling a chemical structure as a chemical graph and quantitatively analyzing the related graph using molecular descriptors. Molecular descriptors are useful in many areas of chemistry, biology, computer science, and other sciences because they allow for the analysis of chemical structures without the need for experiments. In this work, the quotient graph approach is used to establish the distance based descriptors of symmetrically configured two-pentagonal and three-pentagonal carbon nanocones.