Graph-based Algorithm Research Articles

Unsupervised hindi word sense disambiguation using graph based centrality measures

The task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset.

FedGE: Break the Scalability Limitation of Graph Neural Network With Federated Graph Embedding

Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

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.

Predicting Feynman periods in ϕ4-theory

We present efficient data-driven approaches to predict the value of subdivergence-free Feynman integrals (Feynman periods) in ϕ4-theory from properties of the underlying Feynman graphs, based on a statistical examination of almost 2 million graphs. We find that the numbers of cuts and cycles determines the period to better than 2% relative accuracy. Hepp bound and Martin invariant allow for even more accurate predictions. In most cases, the period is a multi-linear function of the properties in question. Furthermore, we investigate the usefulness of machine-learning algorithms to predict the period. When sufficiently many properties of the graph are used, the period can be predicted with better than 0.05% relative accuracy.We use one of the constructed prediction models for weighted Monte-Carlo sampling of Feynman graphs, and compute the primitive contribution to the beta function of ϕ4-theory at L ∈ {13, … , 17} loops. Our results confirm the previously known numerical estimates of the primitive beta function and improve their accuracy. Compared to uniform random sampling of graphs, our new algorithm is 1000-times faster to reach a desired accuracy, or reaches 32-fold higher accuracy in fixed runtime.The dataset of all periods computed for this work, combined with a previous dataset, is made publicly available. Besides the physical application, it could serve as a benchmark for graph-based machine learning algorithms.

Outpatient scheduling problem in smart hospital with two-agent deep reinforcement learning algorithm

As the internet and digital technologies revolutionize the healthcare sector, the development of smart hospitals is pivotal for improving medical service efficiency. This research presents a mathematical model for the outpatient scheduling problem and introduces the Graph-based Two-Agent Deep Reinforcement Learning algorithm (GTARS-DRL) to address the escalating demand for medical services and the scarcity of resources. The GTARS-DRL leverages graph neural networks for feature extraction of patient medical activities and applies the PDQPN algorithm to train agents for optimizing patient sequencing and service desk allocation, effectively minimizing wait times. Our experiments indicate that GTARS-DRL outperforms traditional scheduling rules in solution quality and matches the performance of Genetic Algorithms (GA), with a notable advantage in computational efficiency, boasting an ACPU of 0.762 s and an ARPT of 0.327 s. The algorithm’s efficacy is further supported by its successful application to larger-scale instances, showcasing its potential for real-world use. This study contributes a novel approach to outpatient scheduling in smart hospitals, providing theoretical backing and practical direction for the optimization of medical resource allocation, and is poised to significantly enhance service efficiency and patient satisfaction.

MoPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides.

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen .

Secure communication in the digital age: a new paradigm with graph-based encryption algorithms

Secure communication in the digital age: a new paradigm with graph-based encryption algorithms

An improved Graph-based SNR Estimation Algorithm

An improved Graph-based SNR Estimation Algorithm

Discover the Factors of Longevity by Causal Discovery with PC Algorithm and Neural Networks

The study title has been to study the multilayered factors that govern a human prospect of living through the application of the PC Algorithm (Graph-Based Algorithm), logistic regression analysis, and the neural networks in combination. To realign the research purpose, a vast array of data can be used, which included socioeconomic status information alongside health and environmental aspects carefully chosen for their likely influence on how long people can live. This article will explain the longevity determinants by examining not only the conditions but also the environmental and health indicators and their combinatory effects. The PC algorithm can produce a directed acyclic graph (DAG) to see how elements like healthcare spending, education, and CO2 emissions are correlated with human longevity and how they influence each other. Moreover, the research takes other examples by analysis: researchers can examine hypothetical events. Neural networks were employed to depict the interrelationship among these variables which subsequently provided a user-appropriate understanding of their sudden joint influence. Consequently, a hypothesize was purposed that the potential interventions for the elongation of human life. Policy-making researchers can gain a better representation of the situation of health inequality and the new recommended solutions to tackle this problem and increase life expectancy by implementing complex analysis tools to blows away the criminal aspects of human longevity.

Improved protein interaction models predict differences in complexes between human cell lines.

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Stereo and LiDAR Loosely Coupled SLAM Constrained Ground Detection.

In many robotic applications, creating a map is crucial, and 3D maps provide a method for estimating the positions of other objects or obstacles. Most of the previous research processes 3D point clouds through projection-based or voxel-based models, but both approaches have certain limitations. This paper proposes a hybrid localization and mapping method using stereo vision and LiDAR. Unlike the traditional single-sensor systems, we construct a pose optimization model by matching ground information between LiDAR maps and visual images. We use stereo vision to extract ground information and fuse it with LiDAR tensor voting data to establish coplanarity constraints. Pose optimization is achieved through a graph-based optimization algorithm and a local window optimization method. The proposed method is evaluated using the KITTI dataset and compared against the ORB-SLAM3, F-LOAM, LOAM, and LeGO-LOAM methods. Additionally, we generate 3D point cloud maps for the corresponding sequences and high-definition point cloud maps of the streets in sequence 00. The experimental results demonstrate significant improvements in trajectory accuracy and robustness, enabling the construction of clear, dense 3D maps.

Distributed multi-object tracking under limited field of view heterogeneous sensors with density clustering

We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with synthetic and real-world trajectory datasets demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.

Graph total variation and low-rank regularization for heterogeneous change detection

Heterogeneous change detection (HCD) is challenging because different imaging mechanisms for various sensors make images difficult to compare directly. To address this problem, a graph-based regression algorithm is proposed for HCD, by leveraging the Graph Total Variation regularization and Low-Rank matrices decomposition (GTVLR). Utilizing graph signal processing (GSP) theory, a directed graph (digraph) model is employed to effectively represent the orientation and correlation information of images, thereby enabling direct comparison of heterogeneous data within the same domain after graph filtering. The GTVLR framework facilitates the decomposition of post-event images into regression and changed images. This decomposition ensures that the regression image mirrors the structure similarity of the pre-event image, while the changed image highlights areas of alteration, aiding in change detection. The model characterizes the piecewise smoothness and Low-Rank properties of data through GTV regularization and Low-Rank penalty, respectively. Moreover, by integrating the higher-order neighboring information within the digraph to refine the model. Experiments conducted on three real-world datasets and comparison with several state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Research on the path planning integrated with multi-steer modes for heavy reconfigurable unit

Heavy reconfigurable units (HRUs) can form the heavy transportation vehicular platform (HTVP) to perform transportation for large equipment, by combining with each other. With all-wheel-independent-steered, the HRU can realize multi-steer modes (MSMs) such as Ackermann steer mode (ASM), diagonal steering mode (DSM), and zero radius steer mode (ZSM). However, with heavy weight and long wheelbase, the HRU’s mobility is poor if it only employs ASM. Therefore, this paper proposes a hierarchical path planning architecture, realizing path planning integrated with the above steer modes. This architecture includes the MSMs behaviors planning layer (upper layer) and smooth path planning layer (low layer). Firstly, this paper innovative proposes the MSM graph-based search algorithm in the upper layer, which extends nodes through graph-based searching on the grid map considering MSMs information. Then, the upper layer generates MSMs nodes sequence considering improved cost function for MSMs. Secondly, smooth path planning methods for the MSMs are respectively applied to the ASM sequence, DSM sequence, and ZSM sequence. Comparative simulations are shown as results based on measured parameters from real HRU. The results from the proposed method have the lowest or one of the lowest time cost compared with pure ASM planner and special steer modes planner used in the simulations. The maximum optimization rate can reach 10% or more.

Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

In the field of autonomous robots, achieving complete precision is challenging, underscoring the need for human intervention, particularly in ensuring safety. Human Autonomy Teaming (HAT) is crucial for promoting safe and efficient human–robot collaboration in dynamic indoor environments. This paper introduces a framework designed to address these precision gaps, enhancing safety and robotic interactions within such settings. Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. An integral component of this system is the improved Node Selection Algorithm (iNSA), which utilizes the revised Grey Wolf Optimization (rGWO) for better adaptability and performance. Furthermore, an obstacle tracking model is employed to provide predictive data, enhancing the efficiency of the system. Human insights play a critical role, from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental changes. Extensive simulation and comparison tests confirm the reliability and effectiveness of our proposed model, highlighting its unique advantages in the domain of HAT. This comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

LongT5Rank: A Novel Integrated Hybrid Approach for Text Summarisation

Text summarisation reduces text length while retaining important information, helping individuals, especially students, in managing information overload during research or assignments. However, existing text summarisation methods often lose important details, generate irrelevant or redundant sentences, or produce incoherent summaries. This study introduces a hybrid approach, LongT5Rank (coined in this study), which combines TextRank, an extractive summarisation algorithm, with LongT5, an abstractive summarisation algorithm, to automate the summarisation process. TextRank utilizes GloVe, a pre-trained word embedding model, and PageRank, a graph-based ranking algorithm, to select representative sentences. LongT5, an encoder-decoder transformer model, condenses extracted sentences into a concise and coherent summary, handling input sequences up to 16,384 tokens, for long-range sequence-to-sequence tasks. The LongT5Rank approach has shown significant achievements, including a minimum 60% compression rate, a minimum 0.6 semantic textual similarity score, and an improved F-measure compared to employing TextRank alone. Furthermore, it received positive feedback from Human Level Performance (HLP), underlining the importance of evaluating results directly from human users. This emphasizes the belief that the performance of the proposed solution should be assessed natively by humans. By combining both extractive and abstractive methods, LongT5Rank excels in generating accurate and coherent summaries.

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

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

Investigating the Effectiveness of Graph-Based Algorithms in Identifying Insider Threats and Collusion: A Machine Learning Approach for Anomaly Detection in HR Networks

Investigating the Effectiveness of Graph-Based Algorithms in Identifying Insider Threats and Collusion: A Machine Learning Approach for Anomaly Detection in HR Networks

Developing an integrated HIV clinical and population database in rural south Africa

BackgroundHIV cohort data are crucial for understanding the effects of HIV infection and treatment. Linkage between HIV clinical and population records unlocks potential to understand the cause and consequences of social-economic factors on HIV-care seeking behaviors and mortality outcomes. We created HIV data infrastructure by linking HIV treatment records with routinely collected records. MethodsWe linked HIV cohort data(n=119,434) to Health and Demographic Surveillance(n=256,425), laboratory tests(n=617,314), clinic visits and hospital admissions(n=170,325) from KwaZulu-Natal, an HIV-endemic setting in rural South Africa. We used graph-based probabilistic record linkage algorithm to link the data for HIV patients accessing care at 17 clinics within the Hlabisa sub-district. Africa Health Research Institute (AHRI) has collected health and demographic surveillance data since 2000. AHRI has set up a hospital information system in the district to capture ICD-10 code admissions and discharges. ResultsFrom the 5% sample of 462,524 linkage clusters, the computed F-score was 0.88, sensitivity was 88% and positive predictive value was 91%. 38.5% of the patient ID clusters contained records linked across multiple databases. 36% of the HIV patient ID clusters contained records linked with laboratory tests. 19.5% of the HIV patient ID clusters contained data on demographic surveillance. 12.5% of the HIV patient ID clusters contained records linked with clinic visits and hospital admissions. 4% of patient IDs contained records across all four databases. ConclusionWe have successfully created a cross-sectorial HIV cohort through probabilistic record linkage methods. The cohort provides a platform to answer policy-relevant questions.