Clustering Coefficient Of Network Research Articles

Disrupted Topological Organization of Resting-State Functional Brain Networks in Age-Related Hearing Loss.

PurposeAge-related hearing loss (ARHL), associated with the function of speech perception decreases characterized by bilateral sensorineural hearing loss at high frequencies, has become an increasingly critical public health problem. This study aimed to investigate the topological features of the brain functional network and structural dysfunction of the central nervous system in ARHL using graph theory.MethodsForty-six patients with ARHL and forty-five age, sex, and education-matched healthy controls were recruited to undergo a resting-state functional magnetic resonance imaging (fMRI) scan in this study. Graph theory was applied to analyze the topological properties of the functional connectomes by studying the local and global organization of neural networks.ResultsCompared with healthy controls, the patient group showed increased local efficiency (Eloc) and clustering coefficient (Cp) of the small-world network. Besides, the degree centrality (Dc) and nodal efficiency (Ne) values of the left inferior occipital gyrus (IOG) in the patient group showed a decrease in contrast with the healthy control group. In addition, the intra-modular interaction of the occipital lobe module and the inter-modular interaction of the parietal occipital module decreased in the patient group, which was positively correlated with Dc and Ne. The intra-modular interaction of the occipital lobe module decreased in the patient group, which was negatively correlated with the Eloc.ConclusionBased on fMRI and graph theory, we indicate the aberrant small-world network topology in ARHL and dysfunctional interaction of the occipital lobe and parietal lobe, emphasizing the importance of dysfunctional left IOG. These results suggest that early diagnosis and treatment of patients with ARHL is necessary, which can avoid the transformation of brain topology and decreased brain function.

Sensorineural Hearing Loss Affects Functional Connectivity of the Auditory Cortex, Parahippocampal Gyrus and Inferior Prefrontal Gyrus in Tinnitus Patients.

BackgroundTinnitus can interfere with a patient’s speech discrimination, but whether tinnitus itself or the accompanying sensorineural hearing loss (SNHL) causes this interference is still unclear. We analyzed event-related electroencephalograms (EEGs) to observe auditory-related brain function and explore the possible effects of SNHL on auditory processing in tinnitus patients.MethodsSpeech discrimination scores (SDSs) were recorded in 21 healthy control subjects, 24 tinnitus patients, 24 SNHL patients, and 27 patients with both SNHL and tinnitus. EEGs were collected under an oddball paradigm. Then, the mismatch negativity (MMN) amplitude and latency, the clustering coefficient and average path length of the whole network in the tinnitus and SNHL groups were compared with those in the control group. Additionally, we analyzed the intergroup differences in functional connectivity among the primary auditory cortex (AC), parahippocampal gyrus (PHG), and inferior frontal gyrus (IFG).ResultsSNHL patients with or without tinnitus had lower SDSs than the control subjects. Compared with control subjects, tinnitus patients with or without SNHL had decreased MMN amplitudes, and SNHL patients had longer MMN latencies. Tinnitus patients without SNHL had a smaller clustering coefficient and a longer whole-brain average path length than the control subjects. SNHL patients with or without tinnitus had a smaller clustering coefficient and a longer average path length than patients with tinnitus alone. The connectivity strength from the AC to the PHG and IFG was lower on the affected side in tinnitus patients than that in control subjects; the connectivity strength from the PHG to the IFG was also lower on the affected side in tinnitus patients than that in control subjects. However, the connectivity strength from the IFG to the AC was stronger in tinnitus patients than that in the control subjects. In SNHL patients with or without tinnitus, these changes were magnified.ConclusionChanges in auditory processing in tinnitus patients do not influence SDSs. Instead, SNHL might cause the activity of the AC, PHG and IFG to change, resulting in impaired speech recognition in tinnitus patients with SNHL.

Gut Microbiome Succession in Chinese Mitten Crab Eriocheir sinensis During Seawater-Freshwater Migration.

Biological migration is usually associated with disturbances and environmental changes that are key drivers in determining the diversity, community compositions, and function of gut microbiome. However, little is known about how gut microbiome is affected by disturbance such as salinity changes during migration from seawater to freshwater. Here, we tracked the gut microbiome succession of Chinese mitten crabs (Eriocheir sinensis) during their migrations from seawater to freshwater and afterward using 16S rDNA sequencing for 127 days, and explored the temporal patterns in microbial diversity and the underlying environmental factors. The species richness of gut microbiome showed a hump-shaped trend over time during seawater–freshwater migration. The community dissimilarities of gut microbiome increased significantly with day change. The turnover rate of gut microbiome community was higher during seawater–freshwater transition (1–5 days) than that in later freshwater conditions. Salinity was the major factor leading to the alpha diversity and community dissimilarity of gut microbiome during seawater–freshwater transition, while the host selection showed dominant effects during freshwater stage. The transitivity, connectivity, and average clustering coefficient of gut microbial co-occurrence networks showed decreased trends, while modularity increased during seawater–freshwater migration. For metabolic pathways, “Amino Acid Metabolism” and “Lipid Metabolism” were higher during seawater–freshwater transition than in freshwater. This study advances our mechanistic understanding of the assembly and succession of gut microbiota, which provides new insights into the gut ecology of other aquatic animals.

Co-Comment Network: A Novel Approach to Construct Social Networks within Reddit

This work analyzes the online Reddit comments and proposes a novel approach to construct a social network automatically within Reddit that connects users to it. The investigation reveals that users tend to form links to others who have a mutual interest in interacting with some topics or posts. This mutual interest among users can make connections among them in the network. This study shows that Reddit contains a large, strongly connected core of high-degree nodes, surrounded by many small clusters of low-degree nodes which the authors define as the Co-Comment network. One of the important research focuses is to examine whether the networks built from the Reddit comments show the desirable social network properties, e.g., power-law distribution, small-world effect, and clustering coefficient. Results obtained from the experiment confirm that the Co-Comment network conforms to social network properties. The authors also discover communities within the Co-Comment network and verify them to validate the constructed network.

DEVELOPMENT OF A METHOD FOR CALCULATING THE PROTECTION OF PERSONAL DATA FROM THE CENTRALITY OF THE NETWORK

A mathematical model has been developed and a study of the model of personal data protection from network clustering coefficient and data transfer intensity in social networks has been carried out. Dependencies of protection of the system from the size of the system (and from the amount of personal data); information security threats from the network clustering factor. A system of linear equations is obtained, which consists of the equation: rate of change of information flow from social network security and coefficients that reflect the impact of security measures, amount of personal data, leakage rate, change of information protection from network clustering factor, its size, personal data protection. As a result of solving the system of differential equations, mathematical and graphical dependences of the indicator of personal data protection in the social network from different components are obtained. Considering three options for solving the equation near the steady state of the system, we can conclude that, based on the conditions of the ratio of dissipation and natural frequency, the attenuation of the latter to a certain value is carried out periodically, with decaying amplitude, or by exponentially decaying law. A more visual analysis of the system behavior is performed, moving from the differential form of equations to the discrete one and modeling some interval of the system existence.Mathematical and graphical dependences of the system natural frequency, oscillation period, attenuation coefficient are presented. Simulation modeling for values with deviation from the stationary position of the system is carried out. As a result of simulation, it is proved that the social network protection system is nonlinear.

The research of EEG feature extraction and classification for subjects with different organizational commitment

With the development of EEG analysis technology, researchers have gradually explored the correlation between personality trait (such as Big Five personality) and EEG. However, there are still many challenges in model construction. In this paper, we tried to classify the people with different organizational commitment personality trait through EEG. Firstly, we organized the participants to complete the organizational commitment questionnaire and recorded their resting state EEG. We divided 10 subjects into two classes (positive and negative) according to the questionnaire scores. Then, various EEG features including power spectral density, microstate, functional brain network and nonlinear features from segmented EEG sample were extracted as the input of different machine learning classifiers. Next, several evaluation metrics were used to evaluate the results of the cross-validation experiment. Finally, the results show that the EEG power in α band, the weighted clustering coefficient of functional brain network and the Permutation Entropy of EEG are relatively good features for this classification task. Furthermore, the highest classification accuracy rate can reach 79.9% with 0.87 AUC (the area under the ROC). The attempts in this paper may serve as the basis for our future research.

A Longitudinal Study of Semantic Networks in Schizophrenia and other Psychotic Disorders Using the Word Association Task.

The underpinnings of language deviations in psychotic symptoms (eg, formal thought disorder, delusions) remain unclear. We examined whether the semantic networks underlying word associations are useful predictors of clinical outcomes in psychosis. Fifty-one patients with schizophrenia and other psychotic disorders and 51 matched healthy controls generated words in a Cantonese continued word association task. Patterns of word associations were examined using semantic similarity metrics derived from word embeddings (fastText) and the structure of individual semantic networks. A longitudinal design-baseline and 6 months later-enabled investigation of the relationship of changes in semantic associations in patients who were in an acute psychotic state at baseline compared to clinical stabilization 6 months later. The semantic similarity measure increased over time in patients, while it remained stable in controls. Moreover, the change in semantic similarity over time correlated with the changes in patients' formal thought disorder symptoms. There were differences in individual semantic networks between the groups at both time points. Patients had less structured networks on average, as evidenced by a smaller network diameter and clustering coefficient, and smaller average shortest path lengths. The identification of several state-like semantic measures that change over time with patients' mental states allows for nuanced comparison with clinical measures. Semantic measures are complex. Semantic similarity was a state-like measure that changed over time with mental state in psychotic disorders, whereas individual semantic network parameters were trait-like and stable over time.

Research on the cooperative network of relativity and quantum cosmology researchers

The collaboration networks are classified and studied by analysing various properties of the collaboration data of contributing scholars, and the modules with high information flow are identified for identifying the collaboration patterns of authors in science and technology disciplines. The basic model of the collaboration network is described in terms of statistical parameters such as network degree distribution, clustering coefficients, efficiency and average network path length, and then more efficient core communities of the original network are visualized and analysed after k-core decomposition and modularly identified.The present study shows that authors contributing to the field of general relativity and quantum cosmology tend to cooperate with the same fraction of the population, which results in information transfer efficiency in and between core communities being much greater than the overall network efficiency. In addition, there are also individual authors who prefer to do their research independently.

Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia.

Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer's Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network's graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD.

Electrophysiological Activity and Brain Network During Recovery of Propofol Anesthesia: A Stereoelectroencephalography-Based Analysis in Patients With Intractable Epilepsy—An Exploratory Research

Background: The oscillations and interactions between different brain areas during recovery of consciousness (ROC) from anesthesia in humans are poorly understood. Reliable stereoelectroencephalography (SEEG) signatures for transitions between unconsciousness and consciousness under anesthesia have not yet been fully identified.Objective: This study was designed to observe the change of electrophysiological activity during ROC and construct a ROC network based on SEEG data to describe the network property of cortical and deep areas during ROC from propofol-induced anesthetic epileptic patients.Methods: We analyzed SEEG data recorded from sixteen right-handed epileptic patients during ROC from propofol anesthesia from March 1, 2019, to December 31, 2019. Power spectrum density (PSD), correlation, and coherence were used to describe different brain areas' electrophysiological activity. The clustering coefficient, characteristic path length, modularity, network efficiency, degrees, and betweenness centrality were used to describe the network changes during ROC from propofol anesthesia. Statistical analysis was performed using MATLAB 2016b. The power spectral data from different contacts were analyzed using a one-way analysis of variance (ANOVA) test with Tukey's post-hoc correction. One sample t-test was used for the analysis of network property. Kolmogorov-Smirnov test was used to judge data distribution. Non-normal distribution was analyzed using the signed rank-sum test.Result: From the data of these 16 patients, 10 cortical, and 22 deep positions were observed. In this network, we observed that bilateral occipital areas are essential parts that have strong links with many regions. The recovery process is different in the bilateral cerebral cortex. Stage B (propofol 3.0-2.5 μg/ml) and E (propofol 1.5 μg/ml-ROC) play important roles during ROC exhibiting significant changes. The clustering coefficient gradually decreases with the recovery from anesthesia, and the changes mainly come from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient are similar. Deep areas tend to form functional modules. The left occipital lobe, the left temporal lobe, bilateral amygdala are essential nodes in the network. Some specific cortical regions (i.e., left angular gyrus, right angular gyrus, right temporal lobe, left temporal lobe, and right angular gyrus) and deep regions (i.e., right amygdala, left cingulate gyrus, right insular lobe, right amygdala) have more significant constraints on other regions.Conclusion: We verified that the bilateral cortex's recovery process is the opposite, which is not found in the deep regions. Significant PSD changes were observed in many areas at the beginning of stop infusion and near recovery. Our study found that during the ROC process, the modularity and clustering coefficient of the deep area network is significantly improved. However, the changes of the bilateral cerebral cortex were different. Power spectrum analysis shows that low-frequency EEG in anesthesia recovery accounts for a large proportion. The changes of the bilateral brain in the process of anesthesia recovery are different. The clustering coefficient gradually decreased with the recovery from anesthesia, and the changes mainly came from the cortical region. The characteristic path length and network efficiency do not change significantly during the recovery from anesthesia, and the changes of network modularity and clustering coefficient were similar. During ROC, the left occipital lobe, the left temporal lobe, bilateral amygdala were essential nodes in the network. The findings of the current study suggest SEEG as an effective tool for providing direct evidence of the anesthesia recovery mechanism.

How heterogeneity in connections and cycles matter for synchronization of complex networks.

We analyze how the structure of complex networks of non-identical oscillators influences synchronization in the context of the Kuramoto model. The complex network metrics assortativity and clustering coefficient are used in order to generate network topologies of Erdös-Rényi, Watts-Strogatz, and Barabási-Albert types that present high, intermediate, and low values of these metrics. We also employ the total dissonance metric for neighborhood similarity, which generalizes to networks the standard concept of dissonance between two non-identical coupled oscillators. Based on this quantifier and using an optimization algorithm, we generate Similar, Dissimilar, and Neutral natural frequency patterns, which correspond to small, large, and intermediate values of total dissonance, respectively. The emergency of synchronization is numerically studied by considering these three types of dissonance patterns along with the network topologies generated by high, intermediate, and low values of the metrics assortativity and clustering coefficient. We find that, in general, low values of these metrics appear to favor phase locking, especially for the Similar dissonance pattern.

How dam construction affects the activity of alkaline phosphatases in reservoir sediments: A study of two highly regulated rivers

Dam construction causes phosphorus (P) accumulation in reservoir sediments and significantly affects the generation of available P. However, the effect of dam construction on the activity of sediment alkaline phosphatase (ALP), which is encoded by the bacterial phoD gene and participates in P mineralization, in river sediments remains unclear. Here, we investigated the ALP activities in 78 sediment samples collected from the cascade reservoir regions located in the Lancang River and the Jinsha River, two highly regulated rivers in southwestern China. The abundance and community composition of phoD-harboring bacteria were determined based on the phoD gene using quantitative real-time PCR and MiSeq sequencing, respectively. Comparison of control and affected sites indicated that dam construction significantly increased sediment ALP activity in both rivers. The abundances of phoD-harboring bacteria increased and their community compositions varied in response to dam construction; the relative abundances of the dominant genera Methylobacterium and Bradyrhizobium were particularly higher in affected site than control site. Co-occurrence network analyses revealed much higher network connectivity and relative abundances of keystone species in affected sites. Some microbial factors including phoD-harboring bacterial abundances, network clustering coefficients, and relative abundance of keystone species were positively correlated with ALP activity. The relative abundance of keystone species was identified as the most important microbial factor contributing to variation in ALP activity based on structural equation modeling analysis. These findings enhance our understanding of how dam construction affects the functions of phoD-harboring bacteria and their role in the P biogeochemical cycle in highly regulated rivers.

Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation.

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

Effect of SSRIs on Resting-State Functional Brain Networks in Adolescents with Major Depressive Disorder.

Investigation of brain changes in functional connectivity and functional network topology from receiving 8-week selective serotonin reuptake inhibitor (SSRI) treatments is conducted in 12 unmedicated adolescents with major depressive disorder (MDD) by using wavelet-filtered resting-state functional magnetic resonance imaging (fMRI). Changes are observed in frontal-limbic, temporal, and default mode networks. In particular, topological analysis shows, at the global scale and in the 0.12–0.25 Hz band, that the normalized clustering coefficient and smallworldness of brain networks decreased after treatment. Regional changes in clustering coefficient and efficiency were observed in the bilateral caudal middle frontal gyrus, rostral middle frontal gyrus, superior temporal gyrus, left pars triangularis, putamen, and right superior frontal gyrus. Furthermore, changes of nodal centrality and changes of connectivity associated with these frontal and temporal regions confirm the global topological alternations. Moreover, frequency dependence is observed from FDR-controlled subnetworks for the limbic-cortical connectivity change. In the high-frequency band, the altered connections involve mostly frontal regions, while the altered connections in the low-frequency bands spread to parietal and temporal areas. Due to the limitation of small sample sizes and lack of placebo control, these preliminary findings require confirmation with future work using larger samples. Confirmation of biomarkers associated with treatment could suggest potential avenues for clinical applications such as tracking treatment response and neurobiologically informed treatment optimization.

The development of tumour vascular networks

The growth of solid tumours relies on an ever-increasing supply of oxygen and nutrients that are delivered via vascular networks. Tumour vasculature includes endothelial cell lined angiogenesis and the less common cancer cell lined vasculogenic mimicry (VM). To study and compare the development of vascular networks formed during angiogenesis and VM (represented here by breast cancer and pancreatic cancer cell lines) a number of in vitro assays were utilised. From live cell imaging, we performed a large-scale automated extraction of network parameters and identified properties not previously reported. We show that for both angiogenesis and VM, the characteristic network path length reduces over time; however, only endothelial cells increase network clustering coefficients thus maintaining small-world network properties as they develop. When compared to angiogenesis, the VM network efficiency is improved by decreasing the number of edges and vertices, and also by increasing edge length. Furthermore, our results demonstrate that angiogenic and VM networks appear to display similar properties to road traffic networks and are also subject to the well-known Braess paradox. This quantitative measurement framework opens up new avenues to potentially evaluate the impact of anti-cancer drugs and anti-vascular therapies.

Public Space Layout Optimization in Affordable Housing Based on Social Network Analysis

The efficient use of public space in affordable housing is of great significance to the physical and mental health of low-income and aging residents. Previous studies have evaluated the layout and quality of public space in residential areas based on residents’ subjective satisfaction, however, there still lack studies exploring residents’ behavior patterns and the use of public spaces based on objective measurement standards. Therefore, this paper selected the public space in the large affordable housing areas in the suburbs as the research object and used social network analysis (SNA) to objectively evaluate the network density, clustering coefficient and small-world value of the public space in affordable housing from the perspective of the physical spatial network of the built public space. Based on the network structure characteristics of existing public spaces, this paper further explores the relationship between the frequency of public space use in and the characteristics of nodes’ social networks and their own attributes, and the influence of public space layout structure on the behavioral patterns of affordable housing residents. This paper puts forward proposals for the renovation and optimization of public space according to the behavioral preferences of affordable housing residents, so as to complete the network of public space, promote the interaction and communication of residents in the residential area, enhance the residents’ experience of using public space and improve the living standard of residents in the residential area.

Information transmission between large shareholders and stock volatility

Does information transmission between large shareholders exacerbate stock volatility or reduce stock volatility? In this study, based on the common shareholding data in the Chinese stock market from 2007 to 2017, we establish the shareholder’s coshareholding network, and use the weighted network clustering coefficient to measure the information transmission speed. Moreover, we investigate the impact of information transmission speed on stock volatility. The empirical results show that a nonlinear U-shaped relationship exists between information transmission speed and stock volatility. The findings are robust to the inclusion of other determinants of stock volatility identified in the literature. Finally, we verify the existence of private information interaction in the coshareholding network.

Altered cerebrocerebellar functional connectivity in patients with obstructive sleep apnea and its association with cognitive function.

Previous functional MRI studies have reported altered brain networks in patients with obstructive sleep apnea (OSA). However, the extent and pattern of abnormal connectivity were inconsistent across studies, and cerebrocerebellar connections have been rarely assessed. We investigated functional network changes in cerebral and cerebellar cortices of OSA patients. Resting-state functional MRI, polysomnography, and neuropsychological (NP) test data were acquired from 74 OSA patients (age: 45.8 ± 10.7 years) and 33 healthy subjects (39.6 ± 9.3 years). Connectivity matrices were extracted by computing correlation coefficients from various regions of interest, and Fisher r-to-z transformations. In the functional connections that showed significant group differences, linear regression was conducted to examine the association between connectivity and clinical characteristics. Patients with OSA showed reduced functional connectivity (FC) in cerebrocerebellar connections linking different functional networks, and greater FC in cortical between-network connections in prefrontal regions involving the default mode network (DMN) and the control network. For OSA group, we found no correlation between FC and sleep parameters including lowest SaO2 and arousal index in the connections where significant associations were observed in healthy subjects. FC changes in DMN areas were related to reduced verbal fluency in OSA. Lower local efficiency and lower clustering coefficient of the salience network in the left cerebellum were also observed in OSA. OSA affects mainly the cerebrocerebellar pathway. The disruption of function in these connections are related to sleep fragmentation and hypoxia during sleep. These abnormal network functions, especially DMN, are suggested to participate in cognitive decline of OSA.

The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier

BackgroundAmnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer’s disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD. New methodIn this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls. ResultsOur results reveal a classification accuracy and AUC of 66.6 ± 1.7% and 0.7475 and 80.0 ± 2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6 ± 2.6% and 0.8295, respectively. Comparison with existing methodsThe analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain. ConclusionIn this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.

ECLP: Friend Recommendation Using Ensemble Approach for Detecting Communities Performing Link Prediction

Social networks provide a variety of online services that play an important role in new connections among members to share their favorite media, document, and opinions. For each member, these networks should precisely recommend (predict) the link of members with the highest common interests. Because of the huge volume of users with different types of information, these networks encounter challenges such as dispersion and accuracy of link prediction. Moreover, networks with numerous users have the problem of computational and time complexity. These problems are caused because all the network nodes contribute to calculations of link prediction and friend suggestions. In order to overcome these drawbacks, this paper presents a new link prediction scheme containing three phases to combine local and global network information. In the proposed manner, dense communities with overlap are first detected based on the ensemble node perception method which leads to more relevant nodes and contributes to the link prediction and speeds up the algorithm. Then, these communities are optimized by applying the binary particle swarm optimization method for merging the close clusters. It maximizes the average clustering coefficient (ACC) of the whole network which results in an accurate and precise prediction. In the last phase, relative links are predicted by Adamic/Adar similarity index for each node. The proposed method is applied to Astro-ph, Blogs, CiteSeer, Cora, and WebKB datasets, and its performance is compared to state-of-the-art schemes in terms of several criteria. The results imply that the proposed scheme has a significant accuracy improvement on these datasets.