Efficient Graph Research Articles

Learning symmetry-aware atom mapping in chemical reactions through deep graph matching

Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions. Notably, AMNet incorporates the consideration of molecule symmetry, enhancing accuracy while simultaneously reducing computational complexity. The integration of the Weisfeiler-Lehman isomorphism test for symmetry identification refines the model’s predictions. Furthermore, our model maps the entire atom set in a chemical reaction, offering a comprehensive approach beyond focusing solely on the main molecules in reactions. We evaluated AMNet’s performance on a subset of USPTO reaction datasets, addressing various tasks, including assessing the impact of molecular symmetry identification, understanding the influence of feature selection on AMNet performance, and comparing its performance with the state-of-the-art method. The result reveals an average accuracy of 97.3% on mapped atoms, with 99.7% of reactions correctly mapped when the correct mapped atom is within the top 10 predicted atoms.Scientific contributionThe paper introduces a novel end-to-end deep graph matching model for atom mapping, utilizing molecular graph representations to capture structural characteristics effectively. It enhances accuracy by integrating symmetry detection through the Weisfeiler-Lehman test, reducing the number of possible mappings and improving efficiency. Unlike previous methods, it maps the entire reaction, not just main components, providing a comprehensive view. Additionally, by integrating efficient graph matching techniques, it reduces computational complexity, making atom mapping more feasible.

Constraints on the trilinear and quartic Higgs couplings from triple Higgs production at the LHC and beyond

AbstractExperimental information on the trilinear Higgs boson self-coupling $$\kappa _3$$ κ 3 and the quartic self-coupling $$\kappa _4$$ κ 4 will be crucial for gaining insight into the shape of the Higgs potential and the nature of the electroweak phase transition. While Higgs pair production processes provide access to $$\kappa _3$$ κ 3 , triple Higgs production processes, despite their small cross sections, will provide valuable complementary information on $$\kappa _3$$ κ 3 and first experimental constraints on $$\kappa _4$$ κ 4 . We investigate triple Higgs boson production at the HL-LHC, employing efficient Graph Neural Network methodologies to maximise the statistical yield. We show that it will be possible to establish bounds on the variation of both couplings from the HL-LHC analyses that significantly go beyond the constraints from perturbative unitarity. We also discuss the prospects for the analysis of triple Higgs production at future high-energy lepton colliders operating at the TeV scale.

Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In the present work, an efficient dynamic graph embedding approach is proposed, called dynamic GCN (DyGCN), which is an extension of the GCN-based methods. The embedding propagation scheme of GCN is naturally generalized to a dynamic setting in an efficient manner, which propagates the change in topological structure and neighborhood embeddings along the graph to update the node embeddings. The most affected nodes are updated first, and then their changes are propagated to further nodes, which in turn are updated. Extensive experiments on various dynamic graphs showed that the proposed model can update the node embeddings in a time-saving and performance-preserving way.

Fast knowledge graph completion using graphics processing units

Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion. To add new relations to the existing knowledge graph by using knowledge graph embedding models, we have to evaluate N×N×R vector operations, where N is the number of entities and R is the number of relation types. It is very costly.In this paper, we provide an efficient knowledge graph completion framework on GPUs to get new relations using knowledge graph embedding vectors. In the proposed framework, we first define transformable to a metric space and then provide a method to transform the knowledge graph completion problem into the similarity join problem for a model which is transformable to a metric space. After that, to efficiently process the similarity join problem, we derive formulas using the properties of a metric space. Based on the formulas, we develop a fast knowledge graph completion algorithm. Finally, we experimentally show that our framework can efficiently process the knowledge graph completion problem.

Infant cry classification using an efficient graph structure and attention-based model

Crying serves as the primary means through which infants communicate, presenting a significant challenge for new parents in understanding its underlying causes. This study aims to classify infant cries to ascertain the reasons behind their distress. In this paper, an efficient graph structure based on multi-dimensional hybrid features is proposed. Firstly, infant cries are processed to extract various speech features, such as spectrogram, mel-scaled spectrogram, MFCC, and others. These speech features are then combined across multiple dimensions to better utilize the information in the cries. Additionally, in order to better classify the efficient graph structure, a local-to-global convolutional neural network (AlgNet) based on convolutional neural networks and attention mechanisms is proposed. The experimental results demonstrate that the use of the efficient graph structure improved the accuracy by an average of 8.01% compared to using standalone speech features, and the AlgNet model achieved an average accuracy improvement of 5.62% compared to traditional deep learning models. Experiments were conducted using the Dunstan baby language, Donate a cry, and baby cry datasets with accuracy rates of 87.78%, 93.83%, and 93.14% respectively.

Embedded Feature Selection on Graph-Based Multi-View Clustering

Recently, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing. However, most existing anchor graph-based clustering methods necessitate post-processing to obtain clustering labels and are unable to effectively utilize the information within anchor graphs. To solve these problems, we propose an Embedded Feature Selection on Graph-Based Multi-View Clustering (EFSGMC) approach to improve the clustering performance. Our method decomposes anchor graphs, taking advantage of memory efficiency, to obtain clustering labels in a single step without the need for post-processing. Furthermore, we introduce the l2,p-norm for graph-based feature selection, which selects the most relevant data for efficient graph factorization. Lastly, we employ the tensor Schatten p-norm as a tensor rank approximation function to capture the complementary information between different views, ensuring similarity between cluster assignment matrices. Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches.

Successive POI Recommendation via Brain-Inspired Spatiotemporal Aware Representation

Existing approaches usually perform spatiotemporal representation in the spatial and temporal dimensions, respectively, which isolates the spatial and temporal natures of the target and leads to sub-optimal embeddings. Neuroscience research has shown that the mammalian brain entorhinal-hippocampal system provides efficient graph representations for general knowledge. Moreover, entorhinal grid cells present concise spatial representations, while hippocampal place cells represent perception conjunctions effectively. Thus, the entorhinal-hippocampal system provides a novel angle for spatiotemporal representation, which inspires us to propose the SpatioTemporal aware Embedding framework (STE) and apply it to POIs (STEP). STEP considers two types of POI-specific representations: sequential representation and spatiotemporal conjunctive representation, learned using sparse unlabeled data based on the proposed graph-building policies. Notably, STEP jointly represents the spatiotemporal natures of POIs using both observations and contextual information from integrated spatiotemporal dimensions by constructing a spatiotemporal context graph. Furthermore, we introduce a successive POI recommendation method using STEP, which achieves state-of-the-art performance on two benchmarks. In addition, we demonstrate the excellent performance of the STE representation approach in other spatiotemporal representation-centered tasks through a case study of the traffic flow prediction problem. Therefore, this work provides a novel solution to spatiotemporal representation and paves a new way for spatiotemporal modeling-related tasks.

PDEC: A Framework for Improving Knowledge Graph Reasoning Performance through Predicate Decomposition

The judicious configuration of predicates is a crucial but often overlooked aspect in the field of knowledge graphs. While previous research has primarily focused on the precision of triples in assessing knowledge graph quality, the rationality of predicates has been largely ignored. This paper introduces an innovative approach aimed at enhancing knowledge graph reasoning by addressing the issue of predicate polysemy. Predicate polysemy refers to instances where a predicate possesses multiple meanings, introducing ambiguity into the knowledge graph. We present an adaptable optimization framework that effectively addresses predicate polysemy, thereby enhancing reasoning capabilities within knowledge graphs. Our approach serves as a versatile and generalized framework applicable to any reasoning model, offering a scalable and flexible solution to enhance performance across various domains and applications. Through rigorous experimental evaluations, we demonstrate the effectiveness and adaptability of our methodology, showing significant improvements in knowledge graph reasoning accuracy. Our findings underscore that discerning predicate polysemy is a crucial step towards achieving a more dependable and efficient knowledge graph reasoning process. Even in the age of large language models, the optimization and induction of predicates remain relevant in ensuring interpretable reasoning.

Scribble-based complementary graph reasoning network for weakly supervised salient object detection

Current salient object detection (SOD) methods rely heavily on accurate pixel-level annotations. To reduce the annotation workload, some scribble-based methods have emerged. Recent works address the sparse scribble annotations by introducing auxiliary information and enhancing local features. However, the impact of long-range dependence between pixels on energy propagation and model performance has not been explored in this field. In this paper, we propose a novel complementary graph reasoning network (CGRNet), which globally infers relationships between salient regions by building graph representations. Specifically, we introduce a dual-stream cross-interactive graph reasoning pipeline to model high-level representations and incorporate efficient graph cooperation unit (GCU) to adaptively select complementary components from the representations. Additionally, considering the lack of structural information in scribble data, we design an edge-oriented module (EOM) to explicitly mine boundary semantics. Finally, we propose a dense fusion strategy (DFS) to aggregate multi-source semantics in a multi-guidance manner for obtaining complete global information. Experimental and visual results on five benchmarks demonstrate the superiority of our proposed CGRNet.

DoppelGanger++: Towards Fast Dependency Graph Generation for Database Replay

A database replay system (DRS) captures workloads on a production system and then replays them in a test system to test various system changes, avoiding any risk before realizing them in production. The dependency graph generation in a DRS is crucial in preserving output determinism while maximizing concurrency. The state-of-the-art dependency graph generation algorithm deployed in a commercial DBMS uses a generate-and-prune strategy. It first generates a dependency graph by performing backward scans for each request in a workload. It then prunes all redundant edges using an expensive, transitive reduction algorithm. However, we notice that this generates a large dependency graph that contains many redundant edges and its worst-case time complexity is quadratic to the number of requests in a workload. In order to solve these challenging problems, we formally propose four classes of dependency graphs for DRSs. We then present a stateful single forward scan algorithm, SSFS, to generate any class of dependency graphs by performing a single scan over all requests while succinctly maintaining states. Here, states refer to information that is stored and maintained for efficient dependency graph generation. We also propose the parallel SSFS to utilize the computation power with multi-core CPUs while balancing the loads. We implemented our DRS in a leading commercial DBMS. Extensive experiments using the TPC-C, SD benchmarks, and a real-world customer workload show that our DRS significantly improves the dependency graph generation time by up to two orders of magnitude, compared to the state-of-the-art.

Encoder–decoder graph neural network for credit card fraud detection

Credit card fraud is a significant problem, with millions of dollars lost each year. Detecting fraudulent transactions is a challenging task due to the large volume of data and the constantly evolving tactics of fraudsters. Likewise any detection problem, it is important to select relevant features that help distinguishing between fraudulent and non-fraudulent transactions. It is also crucial to design a model that effectively captures the relationships between involved entities such as merchants and customers. In this work, a prior stage of feature engineering is carried out. Then, the use of the Graph Neural Networks (GNNs) for credit card fraud detection is explored by leveraging the relationships between customers and merchants. We propose a novel encoder–decoder based GNNs that effectively captures the complex relationships and dependencies within credit card transaction data. The performance of the model is further improved by employing a graph converter for efficient graph data processing and batch normalization for reducing internal covariate shift and accelerating convergence. Our experiments on a large scale dataset delivered promising results that outperform other models in terms of precision, recall, and F1 score.

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction

BackgroundIdentification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data.ResultsIn this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug–disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug–drug, drug–disease, and disease–disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation.ConclusionsRigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug–disease association prediction, which is beneficial for drug repositioning and drug safety research.

Graph plotting of 1-D motion in introductory physics education using scripts generated by ChatGPT 3.5

Introductory physics courses often necessitate students to graphically represent one-dimensional (1-D) motion and derive various physical parameters. Physical quantities are often solved by calculating either slope or the area under the graph. Instructors and students who do not have access to paid software must plot graphs by hand. This paper explores three introductory physics examples related to 1-D motion, and employs ChatGPT 3.5 to generate scripts that will produce graphs from raw data. It can generate scripts, based on accurate user inputs, in free programming languages like Python and/or GNU Octave to enable efficient graph plotting. Examples will be provided on calculating the slope of a distance–time graph and determining the area under a velocity–time graph. Given raw data, ChatGPT 3.5 may exhibit occasional inaccuracies when solving for physical parameters. However, user can communicate in English to modify the output. The author does not endorse the use of ChatGPT for academic misconducts such as assisting students to cheat in problem solving. Instead, ChatGPT is presented as a tool to enhance data visualisation for both instructors and students, which can potentially improve learning environments in introductory physics. This paper lays the groundwork for future initiatives, beyond the plotting of 1-D motion graphs, aiming to integrate artificial intelligence to assist more complex motion studies in physics learning environments.

Knowledge graph embedding in a uniform space

Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.

Exploiting Graphics Processing Units to Speed Up Subgraph Enumeration for Efficient Graph Pattern Mining GraphDuMato

Subgraph selection involves searching an input graph for subgraphs with a certain attribute. Graph pattern mining (GPM) relies on this technique, despite its computing complexity and rapid growth. Problems with uncoalesced access to memory, separation, and strain unbalance make efficient subgraph identification on GPUs a huge problem, given GPUs' success in speeding up operations across multiple industries. It is surprising that these challenges have not received sufficient attention in earlier research. This work presents new methods for effectively building and running subgraph enumeration on GPUs. Optimization of computational resource usage is achieved by combining a warp-centric design with a Depth first search (DFS) style approach. Memory efficiency, execution divergence, and GPU activity parallelization could all be enhanced by integrating these two methods. An affordable load balancing level is also incorporated for the purpose of dispersing work among thread warps, which further decreases GPU idleness. The GraphDuMato system facilitates the utilization of GPM methods with its intuitive application programming interface (API). Testing proves that GraphDuMato can outperform state-of-the-art GPM algorithms on a regular basis and may mine subgraphs with up to twelve trees.

Intelligent Motion Detection Software based on Efficient Spatiotemporal Graph Convolution Algorithm

Exercise and dietary habits are important ways to maintain human health, and many people hope to ensure their own physical health through reasonable exercise and maintenance methods. Sports health analysis algorithms serve this purpose, and most intelligent motion detection software is also designed based on this algorithm. This article suggested that the performance of this algorithm can be improved through efficient spatiotemporal graph convolution, which enhanced its ability to collect data from various sources, analyze user physical conditions and exercise habits, and provide reasonable suggestions. This article also verified through comparative experiments that the motion health analysis algorithm based on efficient spatiotemporal graph convolution had stronger prediction accuracy than a single motion health analysis algorithm. The average prediction accuracy of these two algorithms was 92.93% and 84.81%, respectively. The experimental results fully demonstrated that the method proposed in this paper was very suitable for assisting in the construction of sports health analysis algorithms.

MIDGNWD : Design of an efficient multimodal iterative deep graph network wearable interface for detecting drowsy drivers

Drowsy driving is a significant contributor to road traffic accidents, necessitating the development of robust and efficient detection systems. Existing approaches for detecting drowsy drivers often rely on single modalities such as image-based facial analysis or vehiclebased metrics, which may lack precision and be susceptible to environmental variations. Furthermore, the existing models might be computationally expensive and result in delayed responses. To address these limitations, this paper proposes a novel Multimodal Iterative Deep Graph Network Wearable Interface that leverages a rich set of data including heart rate, respiration, accelerometer movement, location (speed), traffic, and weather data. These data samples are processed through a fusion of different Recurrent Neural Network processes. The Bidirectional Long Short-Term Memory (BiLSTM) which is used to generate augmented features, is fused with Bidirectional Gated Recurrent Unit (BiGRU) which assists in estimating multidomain feature sets. These features are then used to train an efficient Deep Graph Recurrent Network (DGRN), which facilitates the real-time identification of drowsy drivers. The proposed model significantly enhances the precision of drowsy driver detection by 8.5%, accuracy by 9.5%, recall by 8.3%, and Area Under the Curve (AUC) by 4.9%, while also improving specificity by 5.5% and reducing delay by 10.4%. These improvements collectively contribute to a substantial enhancement in road safety, potentially saving lives by preventing accidents due to drowsy driving. The proposed system, with its real-time processing capability and high accuracy, stands as a significant improvement for identifying drowsy drivers, paving the way for safer and smarter transportation systems.

Lightweight Automatic Modulation Classification Based on Efficient Convolution and Graph Sparse Attention in Low-Resource Scenarios

Lightweight Automatic Modulation Classification Based on Efficient Convolution and Graph Sparse Attention in Low-Resource Scenarios

Bridging Diverse Physics and Scales of Knee Cartilage With Efficient and Augmented Graph Learning

Bridging Diverse Physics and Scales of Knee Cartilage With Efficient and Augmented Graph Learning