Improved gravity model for identifying the influential nodes

Identifying the influential nodes in network is essential for network dynamic analysis. In this letter, inspired by the gravity model, we present an improved gravity model (EDGM) to identify the influential nodes in network through the effective distance. Firstly, we calculate the degree of nodes. Then we construct the effective distance combined with the interaction frequency between nodes, so as to establish the effective distance gravity model. Comparing with the susceptible-infected model, the results show that the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 2.36\% for the gravity model. Compared with other methods, the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 11.55%, 17.29%, 7.17% and 10.00% for the degree centrality, betweenness centrality, eigenvector centrality, and PageRank respectively. The results show that the improved gravity model could effectively identify the influential nodes in network.

Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

Analysis of Regular Patterns in Un-weighted Directed Graphs

Time evolving networks tend to have an element of regularity. This regularity is characterized by existence of repetitive patterns in the data sequences of the graph metrics. As per our research, the relevance of such regular patterns to the network has not been adequately explored. Such patterns in certain data sequences are indicative of properties like popularity, activeness etc. which are of vital significance for any network. These properties are closely indicated by data sequences of graph metrics - degree prestige, degree centrality and occurrence. In this paper, (a) an improved mining algorithm has been used to extract regular patterns in these sequences, and (b) a methodology has been proposed to quantitatively analyse the behavior of the obtained patterns. To analyze this behavior, a quantification measure coined as "Sumscore" has been defined to compare the relative significance of such patterns. The patterns are ranked according to their Sumscores and insights are then drawn upon it. The efficacy of this method is demonstrated by experiments on two real world datasets.

Contact network analysis of Covid-19 in tourist areas——Based on 333 confirmed cases in China

The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.

Aberrant Amplitude of Low-Frequency Fluctuation and Degree Centrality within the Default Mode Network in Patients with Vascular Mild Cognitive Impairment

This study aimed to investigate whole-brain spontaneous activities changes in patients with vascular mild cognitive impairment (VaMCI), and to evaluate the relationships between these brain alterations and their neuropsychological assessments. Thirty-one patients with VaMCI and thirty-one healthy controls (HCs) underwent structural MRI and resting-state functional MRI (rs-fMRI) and neuropsychological assessments. The functional alterations were determined by the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC). The gray matter volume (GMV) changes were analyzed using voxel-based morphometry (VBM). Linear regression analysis was used to evaluate the relationships between the structural and functional changes of brain regions and neuropsychological assessments. The VaMCI group had significantly lower scores in the Montreal Cognitive Assessment (MoCA), and higher scores on the Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD). Compared to the HCs, the VaMCI group exhibited GM atrophy in the right precentral gyrus (PreCG) and right inferior temporal gyrus (ITG). VaMCI patients further exhibited significantly decreased brain activity within the default mode network (DMN), including the bilateral precuneus (PCu), angular gyrus (AG), and medial frontal gyrus (medFG). Linear regression analysis revealed that the decreased ALFF was independently associated with lower MoCA scores, and the GM atrophy was independently associated with higher HAMD scores. The current finding suggested that aberrant spontaneous brain activity in the DMN might subserve as a potential biomarker of VaMCI, which may highlight the underlying mechanism of cognitive decline in cerebral small vessel disease.

Differences in Brain Functional Networks of Executive Function Between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals

It remains controversial whether long-term logographic-logographic bilingual experience shapes the special brain functional subnetworks underlying different components of executive function (EF). To address this question, this study explored the differences in the functional connections underlying EF between the Cantonese-Mandarin bilinguals and Mandarin monolinguals. 31 Cantonese-Mandarin bilinguals and 31 Mandarin monolinguals were scanned in a 3-T magnetic resonance scanner at rest. 4 kinds of behavioral tasks of EF were tested. Network-based statistics (NBS) was performed to compare the connectomes of fronto-parietal (FP) and cingulo-opercular (CO) network between groups. The results showed that the bilinguals had stronger connectivity than monolinguals in a subnetwork located in the CO network rather than the FP network. The identified differential subnetwork referred to as the CO subnetwork contained 9 nodes and 10 edges, in which the center node was the left mid-insula with a degree centrality of 5. The functional connectivity of the CO subnetwork was significantly negatively correlated with interference effect in bilinguals. The results suggested that long-term Cantonese-Mandarin bilingual experience was associated with stronger functional connectivity underlying inhibitory control in the CO subnetwork.

The Dynamic Influence of High-Speed Rail on the Spatial Structure of Economic Networks and the Underlying Mechanisms in Northeastern China

Drawing on 185 cities in the northeastern region of China, this paper improves the radiation model by incorporating the accessibility index to characterize the asymmetric process of economic linkages before HSR in 2007 and after HSR in 2016. Then social network analysis is utilized to examine the impact of HSR on the spatial structure of economic networks, including nodal centrality and community structures. Finally, spatial econometric models are employed to explore the driving factors of nodal centrality in economic networks and some policy implications are proposed. The major findings of this paper are the following. First, HSR services can weaken the core-peripheral inequality of economic linkages and a corridor economy is evident in northeastern China. Second, HSR services have significantly improved the out-degree centrality of prefecture-level cities but have slightly decreased the in-degree centrality of Liaoning. Third, there was a slight decline of coherence in the economic network after the construction of HSR and the within-modular connections were strengthened by HSR. Four, the spatial error model (SEM) is more desirable for explaining the distribution of in-degree centrality. GDP, fixed asset investment, education, population, and fiscal expenditure are important contributors to the in-degree centrality in economic networks. These findings give significant insights into city system planning, integrated transport and land use development, formulating regional poles and the coordinated development across administrative boundaries in northeastern China.

Identifying influential spreaders in complex networks by an improved gravity model

Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.

Alteration of Whole Brain ALFF/fALFF and Degree Centrality in Adolescents With Depression and Suicidal Ideation After Electroconvulsive Therapy: A Resting-State fMRI Study

Major depressive disorder (MDD) is one of the most widespread mental disorders and can result in suicide. Suicidal ideation (SI) is strongly predictive of death by suicide, and electroconvulsive therapy (ECT) is effective for MDD, especially in patients with SI. In the present study, we aimed to determine differences in resting-state functional magnetic resonance imaging (rs-fMRI) in 14 adolescents aged 12–17 with MDD and SI at baseline and after ECT. All participants were administered the Hamilton Depression Scale (HAMD) and Beck Scale for Suicide Ideation (BSSI) and received rs-fMRI scans at baseline and after ECT. Following ECT, the amplitude of low frequency fluctuation (ALFF) and fractional ALFF (fALFF) significantly decreased in the right precentral gyrus, and the degree centrality (DC) decreased in the left triangular part of the inferior frontal gyrus and increased in the left hippocampus. There were significant negative correlations between the change of HAMD (ΔHAMD) and ALFF in the right precentral gyrus at baseline, and between the change of BSSI and the change of fALFF in the right precentral gyrus. The ΔHAMD was positively correlated with the DC value of the left hippocampus at baseline. We suggest that these brain regions may be indicators of response to ECT in adolescents with MDD and SI.