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Academic literature on the topic 'MODULARITY SCORE'
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Journal articles on the topic "MODULARITY SCORE"
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Miyauchi, Atsushi, and Yasushi Kawase. "Z-Score-Based Modularity for Community Detection in Networks." PLOS ONE 11, no. 1 (2016): e0147805. http://dx.doi.org/10.1371/journal.pone.0147805.
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Sun, Huaqi, Hui Shu, Fei Kang, and Yan Guang. "ModDiff: Modularity Similarity-Based Malware Homologation Detection." Electronics 12, no. 10 (2023): 2258. http://dx.doi.org/10.3390/electronics12102258.
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In recent years, the number and scale of malicious codes have grown exponentially, posing an increasing threat to cybersecurity. Hence, it is of great research value to quickly identify variants of malware and master their family information. Binary code similarity detection, as a key technique in reverse analysis, plays an indispensable role in malware analysis. However, most existing methods focus on similarity at the function or basic block level, ignoring the modular composition of malware. Implementing similarity detection among malware modules would greatly improve the efficiency and accuracy of homology detection. Inspired by the successful application of deep-learning techniques in program analysis, we propose a binary code module similarity detection method called ModDiff. It abstracts malware into attribute graphs, clusters functions using graph-embedded clustering algorithms to decompose malware into function-based modules, and calculates module similarity using graph-matching algorithms and natural language processing-based function similarity detection algorithms. The experimental results indicated that ModDiff improves the accuracy of module partitioning by 10.8% compared with previous work, and the highest F1 score of 89% is achieved in malware homologation detection. These results demonstrate the effectiveness of ModDiff in detecting and analyzing malware with important application value and development prospects.
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Sachdeva, Shubhangini, Sridhar Mangalesh, and Sharmila Dudani. "Knowledge, Attitude and Practices of Breast Self-Examination Amongst Indian Women: A Pan-India Study." Asian Pacific Journal of Cancer Care 6, no. 2 (2021): 141–47. http://dx.doi.org/10.31557/apjcc.2021.6.2.141-147.
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Objective: Breast Cancer is the leading cause of mortality and morbidity amongst Indian females. Breast self-examination (BSE) as an early detection modularity can have a profound effect on lowering mortality, if it is practiced regularly. The aim of this study was to assess the knowledge, attitude, and practice of Breast Self-examination amongst Indian women, aged 30 years and above. Methods: A validated questionnaire, consisting 35 questions measuring ‘Knowledge, Attitudes and Practice of Breast Self-Examination’ was widely circulated amongst 1000 females throughout India and responses were collected for a duration of 3 weeks, from 2nd, September 2020 to 23rd,September 2020. Results: Despite having knowledge about the technique of performing BSE, the respondents did not have a positive attitude towards it and were reluctant to practice BSE. A total of 54.4%, 15.1%, 21.3% and 9.2% of respondents were from the Northern, Southern, Western and Eastern regions of India, respectively. The overall median (IQR) KAP score was 70.0 (16.0) out of a maximum of 110. The knowledge, attitude and practice domain scores were 22.0 (5.0), 36.0 (7.0), and 13.0 (8.0) out of a maximum of 30, 52 and, 28, respectively. All three individual score domains were significantly correlated with each other and the total score.Conclusion: Despite having knowledge about the technique of performing BSE, the respondents did not have a positive attitude towards it and were reluctant to practice BSE.
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De Meo, Federico, Giorgio Cacciola, Vittorio Bellotti, Antongiolio Bruschetta, and Pietro Cavaliere. "Trabecular Titanium acetabular cups in hip revision surgery: mid-term clinical and radiological outcomes." HIP International 28, no. 2_suppl (2018): 61–65. http://dx.doi.org/10.1177/1120700018812992.
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Introduction: Severe acetabular bone loss during revision hip arthroplasty can be restored with different surgical techniques. Best results have been observed using porous tantalum cementless cups. Trabecular Titanium (TT) cups were designed to reproduce the structure, mechanical and biological characteristics of trabecular bone. Few clinical studies have been published with these cups. The aim of this study is to evaluate the clinical and radiological outcome of patients with large acetabular defects (Paprosky IIb or higher) revised using TT cups. Materials and methods: 64 consecutive patients who underwent revision for total hip arthroplasty (THA) between 2011 and 2015 at our institution were enrolled. All were classified as Paprosky IIb, IIc, IIIa and IIIb. Periprosthetic joint infections and fractures were not included in this study. Radiographic and clinical (Harris Hip Score (HHS)) outcomes were evaluated. Kaplan-Meyer survivorship curve was performed. Results: Complete data of 58 patients (mean follow-up 48.3 months) were analysed. 6 patients underwent a further revision (10.3%). Causes of re-revision were instability (5.2%), infection (3.4%) and aseptic loosening (1.7%). No radiolucent lines or signs of migration were observed. Survivorship was 94.8%. HHS Improved from a mean of 36.5–83.7. Discussion: TT cups demonstrated encouraging results at mid-term follow-up. Cup modularity represented a helpful tool to restore hip rotation centre and to reduce the risk of dislocation. The capacity of trabecular titanium to induce bone growth and the versatility of modularity, make the Delta TT System a very useful aid for surgeons who are more frequently faced with complex revision.
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Wang, Guohai, Yang Huang, Wei Yao, et al. "Structure and characteristics of the plant-frugivore bird network from the Guilin Botanical Garden." PeerJ 11 (March 16, 2023): e15028. http://dx.doi.org/10.7717/peerj.15028.
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The interaction between plants and frugivores is crucial to ecosystem function and community diversity. However, little is known about the interaction between plants and frugivorous bird species in urban green spaces. We observed interactions between plants and frugivorous birds in the Guilin Botanical Garden for one year and determined the structure and characteristics of the interaction network. We also analyzed the impact of species traits on their network roles. Interactions between 14 frugivorous birds and 13 fruit plant species were recorded in the study area. Autumn interactions comprised 38.79% of the overall network, and winter interactions comprised 33.15%. The modularity (Q, z-score) of the network was higher in autumn; the weighted nestedness (wNODF, z-score) and interaction evenness (E2, z-score) of the network were higher in winter; the connectance (C, z-score) and interaction diversity (z-score) of the network were higher in spring; and the specialization (H2′, z-score) of the network was higher in summer. The observed network showed lower C, lower interaction H2, lower E2 , lower wNODF, higher H2′ and higher Q when compared to the random networks. The bird species most important to network stability were Hemixos castanonotus, Parus venustulus, and Pycnonotus sinensis. The most important plant species were Alocasia macrorrhiza, Cinnamomum camphora, and Machilus nanmu. Of all the bird and plant traits included in this study, only plant color had a significant impact on species strength, with black fruit having a higher species strength. Our results suggest that interaction networks in urban green spaces can be temporally complex and variable and that a network approach can be an important monitoring tool for detecting the status of crucial ecosystem functions.
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Feklicheva, Inna, Ilya Zakharov, Nadezda Chipeeva, et al. "Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity." Brain Sciences 11, no. 1 (2021): 94. http://dx.doi.org/10.3390/brainsci11010094.
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The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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Liu, Zhihua, Hongmei Wang, Guishen Wang, and Yu Zhou. "Link community detection based on ensemble learning." Modern Physics Letters B 34, no. 27 (2020): 2050293. http://dx.doi.org/10.1142/s0217984920502930.
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Overlapping community detection is a hot topic in the research of data mining and graph theory. In this paper, we propose a link community detection method based on ensemble learning (LCDEL). First, we transform graph into line graph and construct node adjacency matrix of line graph. Second, we calculate node distance of line graph through a new distance metric and get node distance matrix of line graph. Third, we use PCA method to reduce dimensions of node distance matrix of line graph. Then, we cluster on the reduced node distance matrix by k-means clustering algorithm. Finally, we convert line graph back into original graph and get overlapping communities of original graph with ensemble learning. Experimental results on several real-world networks demonstrate effectiveness of LCDEL method in terms of Normalized Mutual Information (NMI), Extended Modularity (EQ) and F-score evaluation metrics.
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LU, ZAIXIN, WEILI WU, WEIDONG CHEN, JIAOFEI ZHONG, YUANJUN BI, and ZHENG GAO. "THE MAXIMUM COMMUNITY PARTITION PROBLEM IN NETWORKS." Discrete Mathematics, Algorithms and Applications 05, no. 04 (2013): 1350031. http://dx.doi.org/10.1142/s1793830913500316.
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The community structure detection is an important problem in many areas such as biology network, computer network and social network. The objective of this problem is to analyze the relationships among data via the network topology. In the literature, many works have been done for partitioning a network into communities or clustering data into groups. In this paper, we define a series of conditions for communities and formulate the community detection problem into a combinatorial optimization problem which aims at partitioning a given network into disjoint communities such that all the communities satisfy the community conditions. We show that the maximization version of this problem is [Formula: see text]-hard for general networks under some natural conditions, and we develop a greedy heuristic algorithm for it. We also develop a refine algorithm to improve the modularity score of a community partition, subject to the community conditions.
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Wang, Lu, Yong Quan Liang, Qi Jia Tian, Jie Yang, Chao Song, and Zhuang Wu. "A Community Detection Method Based on Multi-Objective Optimization Method." Applied Mechanics and Materials 571-572 (June 2014): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.177.
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Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.
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Zheng, Gang, Liping Zhang, Long Jiang Zhang, et al. "Altered Modular Organization of Functional Connectivity Networks in Cirrhotic Patients without Overt Hepatic Encephalopathy." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/727452.
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Minimal hepatic encephalopathy (MHE) is associated with changes in functional connectivity. To investigate the patterns of modular changes of the functional connectivity in the progression of MHE, resting-state functional magnetic resonance imaging was acquired in 24 MHE patients, 31 cirrhotic patients without minimal hepatic encephalopathy (non-HE), and 38 healthy controls. Newman’s metric, the modularityQvalue, was maximized and compared in three groups. Topological roles with the progression of MHE were illustrated by intra- and intermodular connectivity changes. Results showed that theQvalue of MHE patients was significantly lower than that of controlsP<0.01rather than that of non-HE patientsP>0.05, which was correlated with neuropsychological test scores rather than the ammonia level and Child-Pugh score. Less intrasubcortical connections and more isolated subcortical modules were found with the progression of MHE. The non-HE patients had the same numbers of connect nodes as controls and had more hubs compared with MHE patients and healthy controls. Our findings supported that both intra- and intermodular connectivity, especially those related to subcortical regions, were continuously impaired in cirrhotic patients. The adjustments of hubs and connector nodes in non-HE patients could be a compensation for the decreased modularity in their functional connectivity networks.