A Route Diversity–Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

Structural characteristics and spatiotemporal changes of a reticular river network based on complex network theory

Transfer penalties in multimodal public transport networks

Since the 21st century, global warming has caused frequent natural disasters, bringing great challenges to the stable operation of power grid. Based on the complex network theory, this paper discusses the importance identification of power grid under natural disasters from perspective of system, and analyses its application in cascading failures. Firstly, complex network models such as small-world model, scale-free model and space-time evolution model, etc. are introduced based on the dynamic and static characteristics of power grid topology. Secondly, in order to identify the importance of power grid, some identification indexes such as degree centrality, average path length, betweenness centrality and node cohesion, etc. are listed from two aspects of social network analysis and system science analysis. Some improved indexes are also elaborated. Thirdly, the advantages and disadvantages of establishing cascading failure models are compared from the perspectives of self-organized criticality theory and complex network theory. And typical cascading failure models such as hidden failure model and isolated centrality model are listed. Finally, the development trend of complex network theory in disaster emergency repair, cascading failure, energy Internet, new power system, and the introduction of artificial intelligence algorithm is discussed.

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

Assessing transfer property and reliability of urban bus network based on complex network theory

Calculating centrality measures has been used to identify Cyber-physical System critical component for some time. However, accurate identification of critical elements within the interconnected system that are vulnerable to system failures and intentional attacks requires not only a reliable model but robust index to dynamic system operating environment. These measures include 1) Node Degree, 2) Node Importance, 3) Betweenness Centrality, 4) Closeness Centrality and 5) Eigenvector Centrality. System component criticalities have been evaluated based on these measures in this paper and their performances and rankings under different network topologies and operating conditions have been compared using 1) Pearson Correlation Coefficient and 2) Spearman Correlation Coefficient. This has enabled the quantification of the optimality of using different centrality measures in different network scenarios. The results suggest that Betweenness and Degree centralities are the most suitable indices for identifying critical electrical power system bus and cyber system node individually. Moreover, the degree of correlation between directed measures has suggested also the necessity of using directional model in Cyber-physical System studies.

Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature.

This study aims to measuring systemic risks using network analysis method to obtain the SIFI rating of Indonesian banks. The author analyzes the systemic risk implied in the Indonesian interbank network during the period from 2011 through 2015 based on various network measures such degree centrality and betweenness centrality. The main findings are as follows: First, interconnectedness in the interbank market is increasing. However, a different condition was significantly found in 2013, where the interconnectedness in the interbank market went downwards compared to the previous year. Second, the degree centrality score showed that government-owned and national private banks tend to be in-degree while foreign private banks tend to be out-degree. Third, there was no bank that consistently continued to have a high betweenness centrality score during the study period.

This study aimed to produce a network structure in Integrated Urban Farming Program in Bandung City to map the involved policy actors to realize a food-smart city. In this study, a mixed method was used with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain a network structure in Integrated Urban Farming toward determining the most important actors, the Social Network Analysis (SNA) approach was also employed through the Gephi application. From this context, the structure emphasized four dimensions, namely Degree, Betweenness, Closeness, and Eigenvector Centralities. The results showed that the actor with the most connections (degree of centrality) and best communication control (betweenness centrality) is Parahyangan Catholic University (academic). At the same time, the actor that plays the most important role (eigenvector) is at the lower level of the government’s Sub-District and Urban Village. This study is useful for explaining the importance of the position of actors in the urban farming policy network, which is the key to the success of a program.

Objective To investigate the small-worldness and the betweenness centrality of the nodes in the brain structural networks and its relationship with the course and the central role of the brain regions in the transmission of information across the whole brain in depression. Methods The diffusion tensor imaging data were obtained from 27 depression patients and 33 healthy controls. The brain structural networks were constructed using the complex network theory. Results The brain structural networks had small-world properties in both groups. When compared with the healthy, the betweenness centrality of the nodes of the networks in depression significantly decreased in right superior frontal gyrus (orbital part) (P=0.00035, region survived critical FDR threshold for multiple comparisons), and left putamen (P=0.00054, region survived critical FDR threshold for multiple comparisons). Significant negative correlation was found between the betweenness centrality of left hippocampus and the course in the depression(r=0.50, P=0.016). Conclusion Both of the brain structural networks in depression patients and normal people have the property of small-worldness. But the central role of orbit frontal cortex and putamen in the transmission of information across the whole brain is declined, and the betweenness centrality of hippocampus is negatively related with the course in the depression. Key words: Depression; Diffusion tensor imaging; Brain structural network; Small-world; Betweenness centrality

Information and communication technology (ICT) outsourcing to suppliers has the potential to enhance buyers’ product innovation and overall performance. However, the impact of technology outsourcing to lower-tier suppliers on buyers’ financial performance in multi-tier supply chain networks remains unclear. Unlike prior dyadic studies that focus on a buyer-tier1 supplier perspective, our study extends the analytical scope to encompass buyer, tier-1, and tier-2 ICT supplier triads using multi-tier supply network data from Capital IQ. Building on existing literature indicating that a buyer’s betweenness centrality negatively affects its financial performance, we further find that tier-1 suppliers’ betweenness centrality also exhibits a negative relationship with buyers’ financial performance. In addition, we provide empirical evidence that a buyer’s timely supplier payment amplifies the positive effect of buyer degree centrality on its financial performance, while alleviating the positive effect of tier-1 suppliers’ degree centrality on buyers’ financial performance. Interestingly, timely supplier payment negatively moderates the association between a buyer’s closeness centrality and its financial performance, but positively moderates the corresponding association between tier-1 supplier closeness centrality and buyer financial performance. Our empirical analysis advances theoretical understanding of lower-tier suppliers and sheds light on the bridging role of tier-1 suppliers in the literature of multi-tier supply chain relationships. These findings highlight the benefits of technology-enabled multi-tier supply network structures and their interactions with buyer-tier1 supplier relationships. This study also provides practical insights for firms to improve financial outcomes through strategic ICT outsourcing to multi-layer suppliers.

This paper analyses the robustness of specific public transport networks. Common attributes and which of them have more influence on the networks’ vulnerability are established. Initially, the structural properties of the networks in two graphical representations (L-Space and P-Space) are checked. Afterwards, the spread of problems (traffic jams, etc.) are simulated, employing a model based on a propagation and recovery mechanism, similar to those used in the epidemiological processes. Next, the size of the largest connected subset of stops of the network (giant component) is measured. What is shown is that the faults randomly happened at stops or links, also displaying that those that occurred in the highest weighted links spread slower than others. These others appear at stops with the largest level of betweenness, degree, or eigenvector centralities and PageRank. The modification of the giant component, when several stops and links are removed, proves that the removal of stops with the highest interactive betweenness, PageRank, and degree centralities has the most significant influence on the network’s integrity. Some equivalences in the degree, betweenness, PageRank, and eigenvector centrality parameters have been found. All networks show high modularity with values of index Q close to 1. The networks with the highest assortativity and lowest average number of stops are the ones which a passenger can use to travel directly to their destination, without any change. The Molloy–Reed parameter is higher than 2 in all networks, demonstrating that high integrity exists in them. All stops were characterized by low k-cores ≤3.

Identifying the vital nodes in networks is of great significance for understanding the function of nodes and the nature of networks. Many centrality indices, such as betweenness centrality (BC), eccentricity centrality (EC), closeness centricity (CC), structural holes (SH), degree centrality (DC), PageRank (PR) and eigenvector centrality (VC), have been proposed to identify the influential nodes of networks. However, some of these indices have limited application scopes. EC and CC are generally only applicable to undirected networks, while PR and VC are generally used for directed networks. To design a more applicable centrality measure, two vital node identification algorithms based on node adjacency information entropy are proposed in this paper. To validate the effectiveness and applicability of the proposed algorithms, contrast experiments are conducted with the BC, EC, CC, SH, DC, PR and VC indices in different kinds of networks. The results show that the index in this paper has a high correlation with the local metric DC, and it also has a certain correlation with the PR and VC indices for directed networks. In addition, the experimental results indicate that our algorithms can effectively identify the vital nodes in different networks.

Lumbar disc herniation (LDH) induced nerve compression syndromes have been a prevalent problem with complex neural mechanisms. Changes in distributed brain areas are involved in the occurrence and persistence of syndromes. The present study aimed to investigate the changes of brain functional network in LDH patients with chronic sciatica using graph theory analysis. A total of thirty LDH adults presenting L4 and/or L5 root (s) compression syndromes (LDH group) and thirty age-, sex-, BMI- and education-matched healthy control (HC group) were recruited for functional MRI scan. Whole-brain functional network was constructed for each participant using Pearson's correlation. Global and nodal properties were calculated and compared between two groups, including small-worldness index, clustering coefficient, characteristic path length, degree centrality (DC), betweenness centrality (BC) and nodal efficiency. Both LDH and HC groups showed small-world architecture in the functional network of brain. However, LDH group showed that nodal centralities (DC, BC and nodal efficiency) increased in opercular part of inferior frontal gyrus; and decreased in orbital part of inferior frontal gyrus, lingual cortex and inferior occipital gyrus. The DC and efficiency in the right inferior occipital gyrus were negatively related with the Oswestry Disability Index in LDH group. In conclusion, the LDH-related chronic sciatica syndromes may induce regional brain alterations involving self-referential, emotional responses and pain regulation functions. But the whole-brain small-world architecture was not significantly disturbed. It may provide new insights into LDH patients with radicular symptoms from new perspectives.