Graph Construction Research Articles

Abstract PO5-25-09: rs2242652 POLYMORPHISM OF THE hTERT GENE IN WOMEN WITH BREAST CANCER

Abstract Introduction: Breast cancer is the most common neoplasm in women around the world. There are several risk factors for this cancer: sex, age, body mass index (BMI), hormone therapy, family history, genetic mutations, among several others. The vast majority of breast tumors are characterized as carcinomas and may originate from ducts or lobules. Immunohistochemistry is related to the expressions of the estrogen receptor (ER) and progesterone receptor (PR), as well as the expression of the human epidermal growth factor receptor 2 (HER2), cell proliferation or the absence of any expression from receptors on the tumor membrane. The telomeres protect the chromosomes natural endings from suffering DNA loss. Telomerase is a ribonuclear protein complex (RNA and protein) that neutralizes telomeres' shortening. Recurrent point mutations in the TERT telomerase promoter have been identified in several cancers, and the rs2242652 polymorphism is a single nucleotide change, and has been described as associated with breast cancer risk. Objective: The objective of this study is to analyze the TERT rs2242652 polymorphism in breast cancer women and to test the correlation of such data with the prognosis and various clinical variables. Methods: The study was conducted as an observational clinical study of women with breast cancer. A blood sample was collected from the patient and PCR was performed to extract the DNA and to analyze the telomerase polymorphisms in this population. To ensure a more descriptive analysis, the data was used in the construction of tables and graphs, that were then studied in a qualitative manner. The associations were analyzed through direct correlation tests and were followed by comparisons of categorization groups. Results and discussion: Fifty-four women presented polymorphism of the telomerase hTERT gene rs2242652. They were divided into two groups according to the A or G allele that confer the GA/AA and GG gene variants. It was found that the GA/AA group had a trend towards more aggressive tumors, presenting 33% triple negative and 22% of tumors with HER2 overexpression, different from the GG group: 19% triple negative and 10% HER2 overexpression. More aggressive T4 tumors, were more expressive in the GA/AA gene variant group representing 20% of the patients, whereas in the GG group they represented only 8%. Conclusion: The data found in this study on the telomerase TERT gene polymorphism rs2242652 with breast cancer refers to a tendency found in tumors that have a more severe prognosis, to associate with the gene variant linked to the A allele of this gene, however a larger sample number is required for statistical confirmation. Citation Format: Miriam Alvares, Elisa Mascarenhas, Joao Matos. rs2242652 POLYMORPHISM OF THE hTERT GENE IN WOMEN WITH BREAST CANCER [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-25-09.

A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping

A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping

Physics-constrained robust learning of open-form partial differential equations from limited and noisy data

Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate candidate library, while noisy observations lead to imprecise evaluations, which in turn result in redundant function terms or erroneous equations. This study proposes a framework to robustly uncover open-form partial differential equations (PDEs) from limited and noisy data. The framework operates through two alternating update processes: discovering and embedding. The discovering phase employs symbolic representation and a novel reinforcement learning (RL)-guided hybrid PDE generator to efficiently produce diverse open-form PDEs with tree structures. A neural network-based predictive model fits the system response and serves as the reward evaluator for the generated PDEs. PDEs with higher rewards are utilized to iteratively optimize the generator via the RL strategy and the best-performing PDE is selected by a parameter-free stability metric. The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training. The traversal of PDE trees automates the construction of the computational graph and the embedding process without human intervention. Numerical experiments demonstrate our framework's capability to uncover governing equations from nonlinear dynamic systems with limited and highly noisy data and outperform other physics-informed neural network-based discovery methods. This work opens new potential for exploring real-world systems with limited understanding.

Construction and application of a knowledge graph for the spatial arrangement of underground powerhouses

Construction and application of a knowledge graph for the spatial arrangement of underground powerhouses

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

Manifold Peaks Nonnegative Matrix Factorization.

Nonnegative matrix factorization (NMF) has attracted increasing interest for its high interpretability in recent years. It is shown that the NMF is closely related to fuzzy k -means clustering, where the basis matrix represents the cluster centroids. However, most of the existing NMF-based clustering algorithms often have their decomposed centroids deviate away from the data manifold, which potentially undermines the clustering results, especially when the datasets lie on complicated geometric structures. In this article, we present a manifold peaks NMF (MPNMF) for data clustering. The proposed approach has the following advantages: 1) it selects a number of MPs to characterize the backbone of the data manifold; 2) it enforces the centroids to lie on the original data manifold, by restricting each centroid to be a conic combination of a small number of nearby MPs; 3) it generalizes the graph smoothness regularization to guide the local graph construction; and 4) it solves a general problem of quadratic regularized nonnegative least squares (NNLSs) with group l0 -norm constraint and further develops an efficient optimization algorithm to solve the objective function of the MPNMF. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed approach.

Turán problems in pseudorandom graphs

AbstractGiven a graph $F$ , we consider the problem of determining the densest possible pseudorandom graph that contains no copy of $F$ . We provide an embedding procedure that improves a general result of Conlon, Fox, and Zhao which gives an upper bound on the density. In particular, our result implies that optimally pseudorandom graphs with density greater than $n^{-1/3}$ must contain a copy of the Peterson graph, while the previous best result gives the bound $n^{-1/4}$ . Moreover, we conjecture that the exponent $1/3$ in our bound is tight. We also construct the densest known pseudorandom $K_{2,3}$ -free graphs that are also triangle-free. Finally, we give a different proof for the densest known construction of clique-free pseudorandom graphs due to Bishnoi, Ihringer, and Pepe that they have no large clique.

A Graph-Based Keyword Extraction Method for Academic Literature Knowledge Graph Construction

In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve the problem that there are no keywords in some documents for several reasons in the process of knowledge graph construction, an improved keyword extraction algorithm called TP-CoGlo-TextRank is proposed by using word frequency, position, word co-occurrence frequency, and a word embedding model. By combining the word frequency and position in the document, the importance of words is distinguished. By introducing the GloVe word-embedding model, which brings the external knowledge of documents into the TextRank algorithm, and combining the internal word co-occurrence frequency in the documents, the word-adjacency relationship is transferred non-uniformly. Finally, the words with the highest scores are combined into phrases if they are adjacent in the original text. The validity of the TP-CoGlo-TextRank algorithm is verified by experiments. On this basis, the Neo4j graph database is used to store and display the academic literature knowledge graph, to provide data support for research tasks such as text clustering, automatic summarization, and question-answering systems.

Seneca: Taint-Based Call Graph Construction for Java Object Deserialization

Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization.

DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs

Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture the high-order correlations among entities. To this end, we propose DHyper, a recurrent Dual Hypergraph neural network for event prediction in TKGs, which simultaneously models the influences of the high-order correlations among both entities and relations. Specifically, a dual hypergraph learning module is proposed to discover the high-order correlations among entities and among relations in a parameterized way. A dual hypergraph message passing network is introduced to perform the information aggregation and representation fusion on the entity hypergraph and the relation hypergraph. Extensive experiments on six real-world datasets demonstrate that DHyper achieves the state-of-the-art performances, outperforming the best baseline by an average of 13.09%, 4.26%, 17.60%, and 18.03% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

Semi-supervised few-shot fault diagnosis driven by multi-head dynamic graph attention network under speed fluctuations

Although graph neural networks (GNNs) have achieved great success in the field of semi-supervised bearing fault diagnosis recently, most GNN-based studies still face the challenge of insufficient labeled samples. Meanwhile, the signals collected from bearings usually have a time offset due to the alternating speed in the actual working conditions, reducing the applicability of traditional GNN diagnosis methods relying on Euclidean distance. And incomplete use of unlabeled data and insufficient exploitation of neighborhood information further reduce the reliability of fault diagnosis models. To solve these problems, a semi-supervised few-shot fault diagnosis method driven by multi-head dynamic graph attention network (MHDGAT) is proposed in this study. First, dynamic time warping (DTW) similarity is introduced in the construction of K-nearest neighbor graph to solve the problem of local time offset caused by speed change. Second, some pseudo labels generated by smoothness assumption are injected into the raw training set, and more comprehensive information can be learned in the process of model training. Then, the presented MHDGAT model can mine rich fault characteristics from different scales by the multi-head attention and extract the most sensitive features due to the proposed dynamic graph mechanism adaptively. Finally, the fault categories can be obtained by a softmax classifier located at the end of the model. The proposed model has been validated in two case studies, and the experimental results indicate that MHDGAT exerts strong performance even under extremely low-label ratios.

Mathematical Connection of Prospective Mathematics Teachers in Constructing Graph-Based Real-life Problem

Mathematical connection ability is one of the important abilities that students should have. Connecting real-world problems to mathematical concepts can make learning mathematics more meaningful for students. In everyday life, there is a lot of information presented in graphs. Graphing competence consists of graph interpretation and graph construction. Even though graph construction is a crucial yet neglected skill. From the previous explanation, the purpose of this study is to describe the ability of students to construct a graph based on a real-life problem. This descriptive qualitative study is collected from 37 prospective mathematics teachers in a university. All students had to answer one question about constructing a graph based on a real-life situation given. The result of this study, students’ mathematical connection between everyday life and mathematics itself was still low. Using indicators in this study: 10.81% of the students could not represent the real-life situation in the graph that they constructed; 75.68% of the students made graphs but many features of the graphs did not represent the situation well; and 13.51% of the students made more representing graph but some features of the graph did not relevant. Therefore, in mathematics learning the teacher should give more experience to the students to explore everyday life situations using mathematics and encourage students to write their thoughts down so that mathematics becomes meaningful for students. Keywords: constructing graph-based real-life problem, mathematical connection, prospective mathematics teachers

A large-scale graph clustering method for cell conditions spatio-temporal localization in aluminum electrolysis

Sensor nodes (industrial nodes) clustering is an efficacious method for gaining local working conditions real-timely in the production process. The existing deep graph neural networks-based industrial nodes clustering studies construct static graphs to describe the potential spatial correlation among industrial nodes, but the temporal correlation among industrial nodes is ignored, resulting in inferior real-time performance. Meanwhile, these deep learning models fail to obtain good clustering results due to the large-scale and noise-containing characteristics of industrial nodes. In this paper, a large-scale graph clustering method (LsGCM) is proposed for cell conditions spatio-temporal localization in aluminum electrolysis to address the above issues. Specifically, a spatio-temporal graph construction method based on industrial process mechanism knowledge and local working condition boundary types statistical feature fusion is proposed for capturing effective spatio-temporal correlations between industrial nodes. Then, hysteresis knowledge of industrial production system is used to guide the nodes merging, thereby avoiding the incorrect merging of time-asynchronous nodes when performing the spatio-temporal region detection of local working conditions. Finally, combined with the actual spatio-temporal location information of industrial nodes, a novel spatio-temporal information expansion method (StIEM) of the sub-spatio-temporal graph describing local working condition is proposed for obtaining spatio-temporal feature information of the neighborhood, which is beneficial to improve the performance of industrial node clustering. Validation studies on a numerical simulation example and multiple real aluminum electrolysis industrial datasets certificate the effectiveness and superiority of the proposed method. In particular, compared with existing graph clustering methods, our method improves the clustering accuracy by 7.79%.

Knowledge Graph Construction and Digital Twin Modeling Integrating Multi-modal Data

Supply chain analytics is focusing more and more attention on industrial logistics. Optimal allocation of assets is severely restricted by the timing inconsistency and geographical instability of industrial logistics assets brought by unpredictability and variability. Unnecessarily lengthy drive distances and lengthy delays are caused by the incapacity to acquire and utilize industrial logistics asset spatial-and-temporal (ST) qualities rationally, which affects the processes' potential to operate sustainably. Thus, for efficient usage of resources in industrial logistics, a novel machine learning (ML)-assisted knowledge graph (ML-KG) design is provided in this work. For evaluating the multi-modal data produced by widely deployed IoT devices, a novel customizable diversified kernelized support vector machine (CDK-SVM) approach is also suggested. After establishing the suggested ML-KG framework for ST integrity in the representation of the digital twin version, links are carried out and reasoning is performed using job data related to industrial logistics. The graph mechanism effectively distributes the industrial logistics resources. Lastly, the outcome proves that the suggested process is successful in the distribution of industrial logistics assets.

The construction of knowledge graphs based on associated STEM concepts in MOOCs and its guidance for sustainable learning behaviors

The construction of knowledge graphs based on associated STEM concepts in MOOCs and its guidance for sustainable learning behaviors

A note on graphs with purely imaginary per-spectrum

In 1983, Borowiecki and Jóźwiak posed the problem “Characterize those graphs which have purely imaginary per-spectrum.” This problem is still open. The most general result, although a partial solution, was given in 2004 by Yan and Zhang, who show that if G is a bipartite graph containing no subgraph which is an even subdivision of K2,3, then it has purely imaginary per-spectrum. Zhang and Li in 2012 proved that such graphs are planar and admit a Pfaffian orientation. In this article, we describe how to construct graphs with purely imaginary per-spectrum having a subgraph which is an even subdivision of K2,3 (planar and nonplanar) using coalescence of rooted graphs.

OHCA-GCN: A novel graph convolutional network-based fault diagnosis method for complex systems via supervised graph construction and optimization

Fault diagnosis is crucial for ensuring the safe and stable operation of complex systems. Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged as a hot research topic due to its ability to effectively handle the association information inherent in the collected dataset. Regarding the method, a fundamental challenge is the construction of the association graph, conventional construction methods struggle to capture the intricate relationships between nodes with complex coupling. Besides, they operate in an unsupervised learning mode, lacking the ability to evaluate and adjust the constructed graph. To handle this challenge, a novel graph convolutional network-based fault diagnosis method via supervised graph construction and optimization (OHCA-GCN) is proposed. Specifically, the supervised orthogonalized-autoencoder is proposed to construct the association graph. Then, a high-order correlation analysis-based graph optimization strategy is proposed to ensure graph sparsity and alleviate the over-smoothing issue. To validate the effectiveness of the proposed method, experiments are carried out on a pulse rectifier dataset from a hardware-in-the-loop platform. The results show that the proposed method exhibits the excellent diagnostic performance compared with the existing methods.

ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ ГРАФОВЫХ НЕЙРОННЫХ СЕТЕЙ КАК ИНТЕЛЛЕКТУАЛЬНЫХ ПОМОЩНИКОВ ПРИ ПОСТРОЕНИИ ДИНАМИЧЕСКИХ КОГНИТИВНЫХ КАРТ

Approaches to the study of complex dynamic systems are considered. Methods of graph construc-tion with the help of neural networks are considered. The possibility of using trained generative neural networks to help in building dynamic cognitive maps is presented

Prediction of students’ performance in online learning using supervised machine learning

PurposeThis study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course.Design/methodology/approachThe exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course’s virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate.FindingsThe study’s findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students’ use of online resources is a crucial element that influences how well they perform in their academics.Originality/valueThe similarity index is as low as 9%.

Semi-automatic Knowledge Graph Construction Based on Deep Learning

The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of < entity, relationship, entity > or < entity, attribute, attribute value > to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.