High Clustering Coefficient Research Articles

Measure functional network and cortical excitability in post-anoxic patients with unresponsive wakefulness syndrome diagnosed by behavioral scales.

Brain assessment shows great values in prognosis, treatment, resource allocation, and decision-making for patients with disorders of consciousness (DOC). However, less research focused on cortical conditions of patients with unresponsive wakefulness syndrome (UWS). We recorded resting-state EEG and TMS-EEG from post-anoxic patients with UWS, diagnosed by repeated Coma Recovery Scale-Revised (CRS-R). Measurements of functional connectivity and networks were performed by phase lock value (PLV) and network parameters of graph theory (average path length, clustering coefficient, and small-world). Global cortical reactivity values (GCRV) were used to assess cortical excitability. The coefficient of variation (CV) presented marked inter-individual variations of PLV (CV = 0.285), network parameters (CV > 0.2), and GCRV (CV = 0.929) within these patients. The patients' PLV and network parameters at theta and alpha bands significantly correlated with their GCRV values. Patients with higher PLV (r = 0.560, 0.406), as well as better preserved network (lower average path length (r = -0.522, -0.483), higher clustering coefficient (r = 0.522, 0.445), and small-world (r = 0.522, 0.445) at theta and alpha bands, presented higher GCRV. The functional connectivity, which is significantly correlated with frontal GCRV, is also mainly located in the frontal region. These correlations were not significant at other frequency bands: Delta, beta, and gamma bands. These findings suggested that the CRS-R-diagnosed post-anoxic patients with UWS had very different cortical conditions. Functional networks and cortical excitability measured by TMS-EEG could complement behavioral assessment to assess these patients' cortical conditions. It provides a deeper understanding of neurophysiological dysfunction in patients with UWS and hints to the clinics that neural-electrophysiological assessment for such patients may be necessary to acquire their brain conditions, which may benefit stratified management for them.

СТРУКТУРНЫЕ ХАРАКТЕРИСТИКИ ПОЛИТИЧЕСКИХ СООБЩЕСТВ В СОЦИАЛЬНОЙ СЕТИ «ВКОНТАКТЕ»: КОММУНИКАТИВНЫЙ АСПЕКТ

The paper presents a research of structural features of informal and official types of political communities within the VK (VKontakte) social network. To collect data from political communities, the author has developed a script that allows parsing data from the VK by the number of friends of each participant within a community (social capital of community participants), as well as getting data on comments left by participants on public pages. Despite the initially different positioning of the two types of communities, political communities of different types are appeared to have a similar social and communicative structures, owing to the topics of communities and their goals. However, informal communities feature a more uneven distribution of participants: there are several leaders in groups forming their clusters inside the public page, and the number of participants with 1 or 2 friends in a group or no friends at all (“observers”) is much higher there. Simultaneously, official public pages have a high density and a quite high clustering coefficient, which suggests that at the right moment those communities' offline political activity can be used to organize people online. Moreover, unofficial public pages embrace a large number of actors with a high degree of intermediaсу.

Characteristics of Class-Scale Social Network Structures of College Students over Time, Driving Factors and Their Implications for Chinese Higher Education

Classes are the basic unit of learning and social activity for college students in higher education. A good social environment at a class scale has a positive effect on fostering the development of teamwork among university students. To assess characteristics of class-scale social network structures of college students over time, this study randomly selected a total of 30 classes of different majors and grades in universities in northern Jiangsu Province, China, and conducted questionnaires on students’ social networks. Social network analysis method was used to model and analyze the social networks of class Interpersonal interaction for students. The results of the study show that there is significant temporal variability in the class-scale social network structure of college students. During freshman and sophomore years, students interacted closely with each other in class, with a high average network clustering coefficient of 0.81, while in junior and senior years, students tended to interact more distantly with each other in class, with the network clustering coefficient dropping to 0.55. This suggests that early collaborative education does motivate junior students to make friends, but this discontinuous collaborative education has a negative impact on senior students’ motivation to make friends, since teamwork education is mostly concentrated in the early years of freshmen in many general universities in China. In addition, roommate relationships, participation in social activities and hometown gatherings influence class-scale socialization. Class-scale socialization prioritizes dormitory or club activities to become friends with the same class, followed by hometown parties. The results of this study confirm that long-term maintenance of cooperative education and multiple guidance for college students to actively make friends and engage in group cooperation in class are conducive to a positive interpersonal interaction at the class scale.

Agglomeration Externalities, Network Externalities and Urban High-Quality Development: A Case Study of Urban Agglomeration in the Middle Reaches of the Yangtze River

The rapid development of the urban network has led to the fact that cities are no longer single individuals, and the network has changed the urban development environment. The interaction between cities has gradually become an important factor for the high-quality development (HQD) of cities. From the perspective of externalities, it is of great significance to explore the impact of agglomeration externalities and network externalities on the HQD of cities to promote the high-quality and sustainable development of the region. Taking the urban agglomeration in the middle reaches of the Yangtze River as an example, this study constructs a theoretical framework to empirically study the influence of agglomeration externalities and network externalities on the HQD of the city. The results show that the integrated network of the urban agglomeration from 2011 to 2020 had a high clustering coefficient and a small average path length with the characteristics of a “small world”. The centrality of urban nodes was hierarchical and had a “pyramid” structure. From 2011 to 2020, the high-quality development level (HQDL) of the urban agglomeration steadily improved and the regional “development gap” gradually narrowed. Wuhan, Changsha, and Nanchang were in a relatively advantageous position in the urban agglomeration. Furthermore, there was a spatial agglomeration effect and a spatial spillover effect in the HQD of urban agglomeration. Network externalities presented difference in different cities, and the influence of agglomeration externalities on HQD presented a u-shaped nonlinear relationship. Network externalities could significantly promote HQD, and the indirect effect of HQD was greater than its direct effect. In addition, factors such as government capacity and level of opening to the outside world also had a significant impact on the HQD of the region.

Deep Learning-Based Community Detection Approach on Bitcoin Network

Community detection is essential in P2P network analysis as it helps identify connectivity structure, undesired centralization, and influential nodes. Existing methods primarily utilize topological data and neglect the rich content data. This paper proposes a technique combining topological and content data to detect communities inside the Bitcoin network using a deep feature representation algorithm and Deep Feedforward Autoencoders. Our results show that the Bitcoin network has a higher clustering coefficient, assortativity coefficient, and community structure than expected from a random P2P network. In the Bitcoin network, nodes prefer to connect to other nodes that share the same characteristics.

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report.

Overall comfort is the priority for the high-speed railway (HSR) passengers, while its influencing factors and mechanism are not yet apparent. According to the source functional brain network and subjective report, this study revealed the potential influencing factors and mechanisms of passengers overall comfort in high-speed railway environments. Here, an ergonomics field test with 20 subjects was conducted where subjective reports and electroencephalography (EEG) were collected. The electric-source imaging and functional connectivity were used to build the source functional brain network from EEG and network indices were extracted. Statistics analysis results showed that static comfort played the most critical role in the overall comfort, followed by emotional valence, emotional arousal, aural pressure comfort, vibration comfort, and noise comfort. Thermal and visual comfort were insignificant due to the well-designed heating, ventilation, and air conditioning (HVAC) and lighting system of HSR. In addition, the source functional brain network of passengers who felt uncomfortable had the higher clustering coefficient, assortativity coefficient and global efficiency, which meant greater activation of brain compared with passengers who were in a state of comfort. According to the local attributes indices analysis, most key brain regions were located in the frontal and hippocampus, which revealed emotion and spatial perception contribute to the whole comfort degradation process. This work proposed novel insights into HSR passengers overall comfort according to subjective and objective methods. Our findings demonstrate emotional regulation and seat improvements are key factors for future improvement of HSR passengers overall comfort.

Soil depth exerts stronger impact on bacterial community than elevation in subtropical forests of Huangshan Mountain

The elevational distribution of bacterial communities in the surface soil of natural mountain forests has been widely studied. However, it remains unknown if microbial communities in surface and sub-surface soils exhibit a similar distribution pattern with elevation. To do so, Illumina HiSeq sequencing was applied to study the alterations in soil bacterial communities of different soil layers, along an altitudinal gradient from 500 to 1100 m on Huangshan Mountain in Anhui Province, China. Our results revealed a significant higher diversity of the bacterial communities in surface soil layers than in subsurface layers. Adonis analysis showed that soil layer had a greater influence on the composition of the bacterial communities than the elevation. The distance-based multivariate linear model suggested that soil labile organic carbon and elevation were the main element influencing the bacterial community composition in surface and subsurface soils, respectively. A remarkable difference appeared between the co-occurrence network structures of bacterial communities in different soil layers. Compared with the subsurface soil, surface soil had more edges, average degree, and much higher clustering coefficient. The two-way ANOVA results highlighted the significant impact of soil layers on the topological properties of the network compared with that of elevation. The keystone species belonged to Rhodospirillaceae in the surface soil, while the OTUs belonged to Actinomycetales in the subsurface soil. Collectively, our results demonstrate that the effects of soil depth on soil bacterial community composition and network properties of subtropical forest in Huangshan Mountain were significantly higher than those of elevation, with different keystone species in different soil layers. These findings can be served as an important basis for better understanding the microbial functions influencing the maintenance of habitat heterogeneity, biodiversity, and ecosystem services in forests ecosystems.

A Mathematical Kinetic Model and Network Analysis for Multicomponent Dissolution Relationships during the Extraction of Natural Products

Traditional Chinese Medicine (TCM) has a long history and typical ethnic traits. Astragalus and Angelica are used in a natural product called a buyang huanwu decoctionand are considered to function as both food and medicine; such products are called a “homology of medicine and food”. In this study, we examined the complex extraction kinetics that occur during the preparation of the natural product BYHWD. Mathematical tools, including the Laplace transformation and Fick’s law, were used to set up kinetic equations for different components in a model of the decoction. We selected the five most important bioactive ingredients of the BYHWD to find the most important speed control component. The intensity and capacity process parameters of the model were determined. A kinetic model was used to quantitatively analyze the dissolution restriction mechanism among the major components. Further, a component–effect network relationship was established to study the interactions of different components during extraction, considering the integrative effect of TCM compositions. Finally, using network pharmacology, certain network parameters were determined through topological analysis. The results indicate that Astragaloside IV exerts the strongest control over the dissolution rates of other components. The BYHWD has a short average path and a high clustering coefficient. The theoretical and experimental results can be used to quantitatively simulate and optimize TCM extraction processes.

The morphological structure and influence factors analysis of China's domestic civil aviation freight transport network

Civil aviation freight transport (CAFT) in China plays an important role in social and economic development. However, research on the morphological structure of China's CAFT network and its influence factors remains rare. In this paper, we focus on this topic by using a new data set of China's domestic routes from 2015 to 2019. With the complex network theory, we make an empirical study on the morphological structure of China's domestic CAFT network, and try to explore the nexus between the attributes of the network and the influence factors. The results show that China's domestic CAFT network is in line with the small world network model, which has a low average path length and a high clustering coefficient. The centrality of the network presents a saddle distribution with dense in the east and west, while sparse in the central. The results also indicate that there are many localized networks dominated by a few core airports. A number of regional hub cities exist, which are mainly located in the west of China. The development of China's CAFT highly depends on the civil aviation passenger transport (CAPT) network, and is highly affected by GDP, the industrial structure and the geographical location of cities. Finally, we give several policy suggestions, including developing the freight dedicated airports, establishing independent freight transport network system, vigorously developing core and intermediate hub cities, as well as constructing the regional network.

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain.

Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

Influence of clustering coefficient on network embedding in link prediction

Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely to perform better given the topological properties of the network. In this paper, we investigate how the clustering coefficient of a network, i.e., the probability that the neighbours of a node are also connected, affects network embedding algorithms’ performance in link prediction, in terms of the AUC (area under the ROC curve). We evaluate classic embedding algorithms, i.e., Matrix Factorisation, Laplacian Eigenmaps and node2vec, in both synthetic networks and (rewired) real-world networks with variable clustering coefficient. Specifically, a rewiring algorithm is applied to each real-world network to change the clustering coefficient while keeping key network properties. We find that a higher clustering coefficient tends to lead to a higher AUC in link prediction, except for Matrix Factorisation, which is not sensitive to the change of clustering coefficient. To understand such influence of the clustering coefficient, we (1) explore the relation between the link rating (probability that a node pair is the missing link) derived from the aforementioned algorithms and the number of common neighbours of the node pair, and (2) evaluate these embedding algorithms’ ability to reconstruct the original training (sub)network. All the network embedding algorithms that we tested tend to assign higher likelihood of connection to node pairs that share an intermediate or high number of common neighbours, independently of the clustering coefficient of the training network. Then, the predicted networks will have more triangles, thus a higher clustering coefficient. As the clustering coefficient increases, all the algorithms but Matrix Factorisation could also better reconstruct the training network. These two observations may partially explain why increasing the clustering coefficient improves the prediction performance.

Spatial-Temporal Evolution Analysis of Carbon Emissions Embodied in Inter-Provincial Trade in China.

Under the support of Multi-Regional Input–Output (MRIO) analysis, this study constructs the Embodied Carbon Emission Transfer Network (ECETN) using the input–output tables of 42 sectors in 31 provinces of China in 2012, 2015, and 2017 and applies a series of complex network measurement indicators and analysis methods to describe its evolution features. The results show that the embodied carbon emission transfers between provinces generally narrow over time. With its high clustering coefficient and short average path length, ECETN has small-world characteristics and behaves sensitively, and changes in individual provinces can quickly spread and affect the entire system. In addition, the clustering effect and the spatial spillover structural properties of ECETN are explored based on the block model analysis. Finally, Quadratic Assignment Procedure (QAP) is used to analyze and quantify the contribution of provincial structural roles to ECETN, and it is found that spatial adjacency and differences in strength-in, strength-out, and betweenness centrality have significant positive effects, while differences in eigenvector centrality, clustering coefficient have significant negative effects. The restructuring of domestic trade can help achieve national emission reduction. These findings can provide more insights for the government to formulate future development directions and policies to reduce emissions further.

Network classification-based structural analysis of real networks and their model-generated counterparts

AbstractData-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine-learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.

FLP-ID: Fuzzy-based link prediction in multiplex social networks using information diffusion perspective

The growing popularity of online social networks is evident nowadays and allows researchers to find solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying the missing links in the social network. The two significant challenges of the link prediction problem are accuracy and efficiency on growing and multiplex networks. Well-known methods for link prediction are the similarity-based methods, which use local, global, and topological features of the network to predict missing links. These approaches ignore critical factors such as different channels of interaction, information diffusion, group norms to form new connections. Therefore, a fuzzy-based link prediction algorithm (FLP-ID) in multiple social networks is proposed using information diffusion. First, FLP-ID generates a multiplex network by combining different types of relationships among users and identifying the community structure. Thereafter, the algorithm computes node and relative relevance for distinct fuzzy criteria under group norms. Finally, the likelihood score of each non-existing link is computed to predict missing links. The experimental results show that the proposed fuzzy algorithm accuracy is better than crisp algorithms over the multiplex network. The prediction rate of FLP-ID with F1-score, AUC, and balanced accuracy is excellent, which are improved compared to related methods up to 30%, 35%, and 30%, respectively, on high density and clustering coefficient datasets under multiplex settings.

Network Reconfiguration Among Cerebellar Visual, and Motor Regions Affects Movement Function in Spinocerebellar Ataxia Type 3.

BackgroundSpinocerebellar ataxia type 3 (SCA3) is a rare movement disorder characterized with ataxia. Previous studies on movement disorders show that the whole-brain functional network tends to be more regular, and these reconfigurations correlate with genetic and clinical variables.MethodsTo test whether the brain network in patients with SCA3 follows a similar reconfiguration course to other movement disorders, we recruited 41 patients with SCA3 (mean age = 40.51 ± 12.13 years; 23 male) and 41 age and sex-matched healthy individuals (age = 40.10 ± 11.56 years; 24 male). In both groups, the whole-brain network topology of resting-state functional magnetic resonance imaging (rs-fMRI) was conducted using graph theory, and the relationships among network topologies, cytosine-adenine-guanine (CAG) repeats, clinical symptoms, and functional connectivity were explored in SCA3 patients using partial correlation analysis, controlling for age and sex.ResultsThe brain networks tended to be more regular with a higher clustering coefficient, local efficiency, and modularity in patients with SCA3. Hubs in SCA3 patients were reorganized as the number of hubs increased in motor-related areas and decreased in cognitive areas. At the global level, small-worldness and normalized clustering coefficients were significantly positively correlated with clinical motor symptoms. At the nodal level, the clustering coefficient and local efficiency increased significantly in the visual (bilateral cuneus) and sensorimotor (right cerebellar lobules IV, V, VI) networks and decreased in the cognitive areas (right middle frontal gyrus). The clustering coefficient and local efficiency in the bilateral cuneus gyrus were negatively correlated with clinical motor symptoms. The functional connectivity between right caudate nucleus and bilateral calcarine gyrus were negatively correlated with disease duration, while connectivity between right posterior cingulum gyrus and left cerebellar lobule III, left inferior occipital gyrus and right cerebellar lobule IX was positively correlated.ConclusionOur results demonstrate that a more regular brain network occurred in SCA3 patients, with motor and visual-related regions, such as, cerebellar lobules and cuneus gyrus, both forayed neighbor nodes as “resource predators” to compensate for normal function, with motor and visual function having the higher priority comparing with other high-order functions. This study provides new information about the neurological mechanisms underlying SCA3 network topology impairments in the resting state, which give a potential guideline for future clinical treatments.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [ChiCTR1800019901].

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.

Studying the Wikipedia Math Essential Pages using Graph Theory Metrics

Abstract COVID-19 pandemic enforced students in schools and universities all around the world to study using the online and blinded learning. In these learning models, students depend on the Internet for information searching of different scientific essentials to improve their skills and to overcome the gap of facing instructors. One of the most popular sources of information is Wikipedia. In this work, we attempt to study the relations of different math essential pages of Wikipedia to find the relation between these topics. A graph has been constructed for these pages. The graph theoretical metrics, such as, centrality, edge weights and clustering coefficient have been extracted of the constructed graph. The extracted values have been investigated to gain more insights of the math topics that should be studied first. The extracted results show that the in-degree property of the articles and the betweenness value of these articles are correlated. Moreover, there is no relation between the in /out-degree of the pages. Finally, the constructed graph has a small average shortest path and a high global cluster coefficient. This proves that the constructed graph follows the small world phenomenon. Keywords: Graph metrics, Math essentials, Gephi, Small world phenomenon, Directed graph

Structural connectivity of the sensorimotor network within the non-lesioned hemisphere of children with perinatal stroke

Perinatal stroke occurs early in life and often leads to a permanent, disabling weakness to one side of the body. To test the hypothesis that non-lesioned hemisphere sensorimotor network structural connectivity in children with perinatal stroke is different from controls, we used diffusion imaging and graph theory to explore structural topology between these populations. Children underwent diffusion and anatomical 3T MRI. Whole-brain tractography was constrained using a brain atlas creating an adjacency matrix containing connectivity values. Graph theory metrics including betweenness centrality, clustering coefficient, and both neighbourhood and hierarchical complexity of sensorimotor nodes were compared to controls. Relationships between these connectivity metrics and validated sensorimotor assessments were explored. Eighty-five participants included 27 with venous stroke (mean age = 11.5 ± 3.7 years), 26 with arterial stroke (mean age = 12.7 ± 4.0 years), and 32 controls (mean age = 13.3 ± 3.6 years). Non-lesioned primary motor (M1), somatosensory (S1) and supplementary motor (SMA) areas demonstrated lower betweenness centrality and higher clustering coefficient in stroke groups. Clustering coefficient of M1, S1, and SMA were inversely associated with clinical motor function. Hemispheric betweenness centrality and clustering coefficient were higher in stroke groups compared to controls. Hierarchical and average neighbourhood complexity across the hemisphere were lower in stroke groups. Developmental plasticity alters the connectivity of key nodes within the sensorimotor network of the non-lesioned hemisphere following perinatal stroke and contributes to clinical disability.

Analysis of the COVID-19 Pandemic’s Impact on the Cruise Shipping Network in the Asian-Australasian Region

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.

Analysis of the COVID-19 Pandemic’s Impact on the Cruise Shipping Network in the Asian-Australasian Region

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.