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Objectives Dengue epidemic is a dynamic and complex phenomenon that has gained considerable attention due to its injurious effects. The focus of this study is to statically analyze the nature of the dengue epidemic network in terms of whether it follows the features of a scale-free network or a random network. Methods A multifarious network of Aedes aegypti is addressed keeping the viewpoint of a complex system and modelled as a network. The dengue network has been transformed into a one-mode network from a two-mode network by utilizing projection methods. Furthermore, three network features have been analyzed, the power-law, clustering coefficient, and network visualization. In addition, five methods have been applied to calculate the global clustering coefficient. Results It has been observed that dengue epidemic follows a power-law, with the value of its exponent γ = −2.1. The value of the clustering coefficient is high for dengue cases, as weight of links. The minimum method showed the highest value among the methods used to calculate the coefficient. Network visualization showed the main areas. Moreover, the dengue situation did not remain the same throughout the observed period. Conclusions The results showed that the network topology exhibits the features of a scale-free network instead of a random network. Focal hubs are highlighted and the critical period is found. Outcomes are important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases. Zika virus and Chikungunya virus can also be modelled and analyzed in this manner.

Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO), 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector) have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia).

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Field of complex network covers different social, technological, biological, scientific collaborative work, communication networks and many others. Among these networks, transportation network is an important indicator to measure the economic growth in any country. In this study different dynamics of Airport Network in Pakistan are analyzed by the complex network methodology. Dataset of air transportation has been collected from Civil Aviation Authority of Pakistan (CAA) and formatted to accomplish the complex network requirements. The network is formed to observe its different properties and compare these with their topological counterparts. In this, network nodes are represented by Airports of Pakistan while flights between them within a week are considered as edges. The behavior of degree distribution is observed as preferential attachment of nodes, which represented that few nodes are controlling overall network which emphasizes that Airport Network in Pakistan (ANP) follows power law. Clustering coefficient displayed the network as a clustered network. Result of short average path length highlights that Airport Network in Pakistan is small-world network. Study also signified the average nearest neighbour degree node, which explained that ANP exhibited disassortative mixing in nature which states that high degree nodes (airports) tend to connect to low degree nodes (airports). Interestingly, is has been observed that it is not necessary that the most connected node is also the most central node in degree centralities.