Centrality 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.

Order Degree and Centrality Analysis of Advanced Fighter Environmental Control System and Thermal Management System

As fighter aircraft become more advanced, traditional air cycle systems have fallen short of meeting the complex environmental control requirements. Advanced fighters such as the F-22 Raptor have responded to these challenges by adopting integrated thermal management systems. These systems feature complex thermodynamic processes and intricate information transmission pathways among various components, creating a sophisticated network structure. To unravel the complexity of such systems, this paper employs a blend of thermodynamics and information theory. We conduct multi-level analysis using structure entropy method and centrality algorithms to explore the information transmission characteristics within these thermodynamic systems. At the system level, we find that variation in environmental parameters have only a 1% impact on order degree, whereas the influence from the system's own structure is more pronounced. At the component level, within the whole system, the varies components in AFT PAO and fuel loops emerge as critical hubs for information transmission. Among these components, the PAO/Fuel stands out as the most important, with closeness and betweenness centrality exceeding that of all other components by at least 11.5% and 29.1%. This study offers a theoretical foundation for the optimization of thermodynamic system structure and layout, viewed through the lens of information theory.

Jun modulates endoplasmic reticulum stress-associated ferroptosis in dorsal root ganglia neurons during neuropathic pain by regulating Timp1

Neuropathic pain (NP) is a complex disorder caused by lesions or diseases affecting the somatosensory nervous system, severely impacting patients' quality of life. Recent studies suggest ferroptosis may be involved in NP induction, but its precise mechanisms remain unclear. We used GO and KEGG pathway enrichment analyses to functionally annotate ferroptosis-related differentially expressed genes (FRDs). Through STRING and the maximum cluster centrality (MCC) algorithm, we identified five hub FRDs (Jun, Timp1, Egfr, Cdkn1a, Cdkn2a). Single-cell analysis revealed significant expression of Jun and Timp1 in neurons. Our study confirmed the association between ferroptosis and endoplasmic reticulum stress (ERS) in NP and validated changes in hub FRD expression across various NP animal models. In vitro experiments demonstrated that Jun regulates neuronal ferroptosis and ERS, particularly by modulating Timp1 expression. Transcription factor prediction and JASPAR binding site analysis elucidated the regulatory network involving Jun. ROC curve analysis of external datasets highlighted the diagnostic potential of hub FRDs and ERS-related differentially expressed genes (ERSRDs) in NP. Using the Comparative Toxicogenomics Database (CTD), we identified estradiol (E2) as a potential therapeutic drug targeting hub FRDs and ERSRDs. Molecular docking predicted its binding sites with Jun and Timp1, and in vivo experiments confirmed that E2 alleviated NP and reversed the expression of Jun and Timp1. This study underscores the crucial role of Jun and Timp1 in the interplay between ferroptosis and ERS, offering new insights and promising avenues for NP treatment.

Source inference for misinformation spreading on hypergraphs

Source inference aims at revealing the seed of the misinformation spreading on social networks, and attracted great attention in the field of network science and cybersecurity. Extensive real-world data analyses have certificated that individual interactions exist pairwise and higher-order interactions, and thus should be described using the hypergraph. Previous studies about the source inference algorithms are mainly focused on simple graphs (i.e., a graph only has pairwise interactions) while neglecting the higher-order interactions. In this article, we propose a dynamical message-passing (DMP) algorithm to infer the misinformation spreading on hypergraphs. As a comparison, we also extend jordan centrality (JC), betweenness centrality (BC), closeness centrality (CC), and eigenvector centrality (EIG) algorithm to hypergraphs. The results show that our proposed DMP algorithm can accurately and effectively infer the propagation source on both artificial and real-world hypergraphs compared to the centrality algorithms. Even if the increase in propagation scale renders the other centrality algorithms completely ineffective, the DMP algorithm can still distinguish the real propagation sources in the form of a rank smaller than 10% of the network size. In general, our DMP algorithm provides an effective solution for inferring the misinformation source on high-order networks.

Product network and origin of common equity factor risks

This paper extends the multisector asset pricing model based on production networks, achieving direct pricing of a firm’s equity assets at a more microscopic level within the product network. An Equilibrium-Aligned PageRank (EA-PR) centrality algorithm is proposed, and it is theoretically proven that EA-PR centrality can reflect the relative value of products and further derive the value of firms. A new equity asset pricing factor is constructed based on the EA-PR centrality of firms. The key findings can be summarized as follows: Firstly, the EA-PR centrality of firms shows a significant positive linear predictability for equity returns, and the returns associated with the EA-PR centrality factor cannot be fully explained by the Fama–French 6 (FF6) factors. Secondly, the EA-PR centrality factor demonstrates significant explanatory power for the FF6 factors and, to some extent, exhibits predictive ability for future returns of the FF6 factors. Thirdly, the product network model can effectively reflect the market competitiveness and growth potential of firms, and firms with high EA-PR centrality have stronger value capture capabilities, improving gross profit margin, cash flow, and investment growth in the future. Finally, this study empirically demonstrates the consistency between the product network model and the pricing theory of the q5 model, which incorporates the expected growth factor.

A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

In the assembly sequence planning (ASP) of aviation products, recalibration of components or sufficient space to assemble subsequent components are critical factors for ensuring product quality. To address this need, a fine-grained ASP (FASP) is defined to take assembly operations as units to plan sequences. Lots of operations have complex sequence constraints that are attended unequally in the FASP. A method based on knowledge graph (KG) and deep reinforcement learning is proposed to plan assembly operations. Firstly, continuous and discrete procedures are defined, and a quantitative characterization method is presented to deduce complex constraints objectively. Then, a dynamic KG is designed to establish and update the information model mainly composed of constraints. Finally, a labeled degree centrality algorithm (LDCA) considers edge labels to minimize the number of assembly tool changes and assembly direction changes for sequences. An improved deep Q-network (IDQN) introduces a convolutional layer to extract local features of technical requirements for planning procedures more efficiently. A helicopter structure assembly is used to verify the effectiveness of the proposed method. The improved algorithms have better performance in solving speed, sequence quality, and convergence speed than ordinary ASP methods, respectively. The fine-grained assembly sequence is more reasonable and feasible by comparing it with the ordinary sequence.

Analyzing breast cancer comorbidities: a network approach using community detection algorithms

Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. To help improve the current breast cancer comorbidity identification by medical personnel and ultimately, lower the number of death cases from breast cancer comorbidity, this research aims to discover the breast cancer comorbidity community, do modularity and similarity-based evaluation, suggest the best semantic similarity measurement and threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden–Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain–Leiden (0.99), RB Pots–Leiden (0.97), and RB Pots–RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed by four algorithms, two diseases are agreed by three algorithms, a disease is agreed by two algorithms, and ten diseases are agreed by an algorithm.

A Novel Approach for the Analysis of Ship Pollution Accidents Using Knowledge Graph

Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec’s word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis.

Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS. Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes. Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes. This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Identification of mitochondria-related biomarkers in childhood allergic asthma

BackgroundThe mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS.MethodsThis research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed.ResultsA total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples.ConclusionNDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

Investigating the multitarget pharmacological mechanism of Rhodiola wallichiana var. cholaensis acting on angina pectoris using combined network pharmacology and molecular docking.

Rhodiola wallichiana var. cholaensis (RW) is one of the traditional Chinese medicinal materials, which is used to treat angina pectoris (AP). However, the possible underlying mechanisms remains unclear. The aim of this study was to explore RW in the treatment of AP and to identify the potential mechanism of the core compounds. In this study, systematic and comprehensive network pharmacology and molecular docking were used for the first time to explore the potential pharmacological mechanisms of RW on AP. First, the relative compounds were obtained by mining the literature, and potential targets of these compounds using target prediction were collected. We then built the AP target database using the DigSee and GeneCards databases. Based on the data, overlapping targets and hub genes were identified with Maximal Clique Centrality (MCC) algorithm in Cytoscape, cytoHubba. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) analysis were performed to screen the hub targets by topology. Molecular docking was utilized to investigate the receptor-ligand interactions on Autodock Vina and visualized in PyMOL. A total of 218 known RW therapeutic targets were selected. Systematic analysis identified nine hub targets (VEGFA, GAPDH, TP53, AKT1, CASP3, STAT3, TNF, MAPK1 and JUN) mainly involved in the complex treatment effects associated with the protection of the vascular endothelium, as well as the regulation of glucose metabolism, cellular processes, inflammatory responses, and cellular signal transduction. Molecular docking indicated that the core compounds had good affinity with the core targets. The results of this study preliminarily identify the potential targets and signaling pathways of RW in AP therapy and lay a promising foundation for further experimental studies and clinical trials.

The Toll-like Receptor-2/4 Antagonist, Sparstolonin B, and Inflammatory Diseases: A Literature Mining and Network Analysis.

Sparstolonin B (SsnB) is characterized as a new toll-like receptor (TLR)-2/4 antagonist. However, the effects of SsnB on different inflammatory diseases have not been systemically reviewed. We investigated the effects of SsnB on inflammatory diseases with data mining and network analysis of literature, including frequency description, cluster analysis, association rule mining, functional enrichment, and protein-protein interaction (PPI) mining. A total of 27 experimental reports were included. The ARRIVE 2.0 guidelines were used to evaluate the quality of animal studies. Frequency analysis revealed 13 different diseases (cardio-cerebrovascular system diseases account for 23.53%), 12 pharmacological effects (anti-inflammatory effect accounts for 53.85%), and 67 therapeutic targets. The overview of investigation sequence of SsnB studies was depicted by Sankey diagram. Cluster analysis classified the therapeutic targets for SsnB into four main categories: (1) NF-κB; (2) IL-1β, IL-6, and TNF-α; (3) TLR2, TLR4, and MyD88; (4) the other targets. Moreover, the Apriori association discovered two main association pairs: (1) TNF-α, IL-1β, and IL-6 and (2) TLR2, TLR4, and MyD88 (support range 33.33-50%, confidence range 83.33-88.89%). Functional enrichment of the therapeutic targets for SsnB showed that the top enriched items in the biological process were mainly the response to lipopolysaccharide (LPS)/bacterial origin and regulation of cytokine production. Finally, the PPI network and hub gene selection by maximal clique centrality (MCC) algorithm indicated the top ranked proteins were TNF-α, IL-1β, IL-6, AKT1, PPAR-γ, TLR4, CCL2, and TLR2. These results emphasized the importance of TLR2/TLR4-MyD88-NF-κB-IL-1β/IL-6/TNF-α pathways as therapeutic targets of SsnB in inflammatory diseases.

텍스트 마이닝과 네트워크 이론을 활용한 권역별 국가하천 점용허가 키워드 분석

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the ‘installation’ keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf’s law statistical distribution with low frequency of occurrence and low distribution ratio.

Hyperbolic decision-making based on RBF for diabetic/COVID-19 iris

The Iris is the most complex tissue of the body that shows thousands of nerves, blood vessels, muscles, and other tissues. This study presents techniques for searching points on the iris by visual query language based on the Gaussian radial basis function (GRBF) and Hyperbolic decision tree; there are several types of iris belonging to diabetic patients; we consider the behavior of COVID-19 on diabetes; our solution detects similarities in symptoms for two different conditions. It can estimate signs of new patterns of diseases on the next cycle of the time interval of illness for virus mutation. It can match the zones in the iris by Delaunay triangulation in d3. js. Then it can design colored models with the same color for spatial diseases. The best algorithm is used for community detection for a group of signs clustered and reducing the error with K-NN by neo4j on the Cypher platform. It detects congestion of infection and inflammation of chronic diseases as a background disease; Covid-19 disorder is illustrated with a centrality algorithm based on a shared relationship for two diseases graph. Our techniques calculate the accuracy of our query design for the target shared symptoms model for new diseases. We reduced complexity algorithms and search time by decreasing the redundancy of data points. We provide the abstract layer in the target model to reduce the dimensionality of data. We present a model from a graph with a description of knowledge that it can generate to determine the target user. Algorithms optimize this model. This model can map the points of the circle into the query and hyperbolic tree, and decide for their classification in the next mutation of diseases. Our solution improved the performance by about 97% in memory usage and time-consuming of search data in complex mixed diseases with different colors and signs.

Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people’s communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the “Big Data.” Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework.

Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis.

Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer. Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in non-small cell lung cancer and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. This gap in treatment options underscores a significant challenge in the field. Our study aimed to identify hub/key genes specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. We performed a comprehensive analysis on KRAS-MT LUAD samples using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis and protein-protein interaction network analysis. Hub genes were identified with the Maximal Clique Centrality algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. In KRAS-MT LUAD, 3122 DEGs were found (2131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from weighted gene co-expression network analysis, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among 20 hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival. The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. LGR4 stands as a promising biomarker in KRAS-MT LUAD prognosis. Contrasting associations in TCGA and GSE72094 datasets reveal the intricate nature of KRAS-MT LUAD. Additional explorations are imperative to grasp the precise involvement of LGR4 in lung adenocarcinoma prognosis, particularly concerning KRAS mutations. These insights could potentially pave the way for targeted therapeutic interventions, addressing the existing unmet demands in this specific subgroup.

Development of Text Summarization Method based on Graph Theory and Malatya Centrality Algorithm

With the advancement of the internet, humanity has gained easy access to a plethora of information. However, to access accurate content, numerous texts and sources must be read. These texts often contain repetitive words and sentences. The abundance of information renders reading texts in their entirety inefficient in terms of time and makes finding suitable content challenging. To overcome these difficulties, various methods have been developed in research on automatic summarization. In the literature, there are numerous methods developed for different purposes in text summarization. Nevertheless, text summarization can generally be divided into two distinct categories: extractive and abstractive summarization. Abstractive algorithms tend to create new sentences by learning from the text. However, this approach prolongs the working process due to the learning phase and the generated sentences may not possess absolute accuracy. On the other hand, extractive methods, if unable to generate new sentences, have the ability to provide faster and completely accurate summaries by selecting sentences that already exist in the text. For these reasons, in our study, the aim is to perform text summarization using graph theory and the Malatya Centrality Algorithm. The Malatya Centrality Algorithm offers a polynomial approach to solving Vertex Cover Problems and is regarded as an effective solution method. It is believed that the Malatya Centrality Algorithm will contribute to graph-based text summarization. The implementation has been developed using the Python programming language, and the obtained results have been evaluated.

The Potential Mechanism of Curcumin in Treating Oral Squamous Cell Carcinoma Based on Integrated Bioinformatic Analysis.

This study explores the effects of curcumin as a therapeutic agent against oral squamous cell carcinoma (OSCC). We acquired the targets of curcumin from three digital databases, including the Comparative Toxicogenomics Database, Search Tool for Interactions of Chemicals, and SwissTargetPrediction. Then, we identified the differentially expressed genes (DEGs) and the weighted gene coexpression network analysis-based key modules using the expression profiles of GSE23558 to acquire the OSCC-related genes. Additionally, the GeneCards and Online Mendelian Inheritance in Man databases were also used to identify the OSCC-related genes. Finally, curcumin-OSCC interaction genes were obtained by overlapping curcumin targets and OSCC-related genes. The enrichment analysis was performed by the ClusterProfiler algorithm and Metascape, respectively. Then, a protein-protein interaction network was created, and the maximal clique centrality algorithm was used to identify the top 10 hub genes. Besides, we examined the expression levels of hub genes in OSCC using The Cancer Genome Atlas database. 927 DEGs were identified, including 308 upregulated ones and 619 downregulated ones. The cluster one-step network construction function of the WGCNA algorithm recognized a soft-thresholding power of 6, and 9083 genes were acquired. 2591 OSCC-related genes were obtained by overlapping the GSE23558-identified genes and the OSCC-related genes from disease target bases. Finally, we identified 70 candidate drug-disease interaction genes by overlapping the disease-related genes with the curcumin target. The enrichment analysis suggested that response to oxidative stress, epithelial cell proliferation, and AGE/RAGE pathway might involve in the effect of curcumin on OSCC. The topologic study identified the ten hub genes, including VEGFA, AKT1, TNF, HIF1A, EGFR, JUN, STAT3, MMP9, EGF, and MAPK3. A significant difference was observed in VEGFA, AKT1, TNF, HIF1A, EGFR, MMP9, EGF, and MAPK3 expression levels between head and neck squamous cell carcinoma and the normal controls. However, no significant difference was observed in JUN (P = 0.14) and STAT3 (P = 0.054). This study provided an overview and basis for the potential mechanism of curcumin against OSCC. The following experiments should be performed to further understand the effectiveness and safety of curcumin in treating OSCC.

Path Merging Based Betweenness Centrality Algorithm in Delay Tolerant Networks

Path Merging Based Betweenness Centrality Algorithm in Delay Tolerant Networks

Influential process nodes identification strategy for aircraft assembly system based on complex network and improved PageRank

In the manufacturing network, the fluctuation of process operation time can result in the delay of the total completion time, which brings a huge challenge to controlling entire production schedule. For aircraft manufacturing industries, the processes that have a significant impact on the completion time of the assembly line need to be identified and monitored in order to ensure the final assembly efficiency and reliability. However, due to the large number of process nodes and the complexity of resource constraints and relationship constraints, traditional node centrality algorithms cannot identify influential nodes accurately. Therefore, based on complex network theory, this paper studies the influential nodes identification problem in the context of assembly lines. Firstly, The Resource-Process Coupling Network for the assembly line is constructed based on the production background and network resource characteristics. Then, based on PageRank algorithm and the idea of resource iteration, Improved PageRank Algorithm (IPRA) is proposed to identify influential process nodes, which brings the resource and time parameters into the allocation rules. Finally, according to simulation results, a comparative analysis of IPRA and existing algorithms is conducted and concludes that our method can better identify influential nodes in actual complex production networks. Furthermore, this paper identifies influential process nodes of aircraft assembly line based on the case of the commercial aircraft manufactory.