Shortest Path Betweenness Centrality Research Articles

Predicting Suitable Areas for African Swine Fever Outbreaks in Wild Boars in South Korea and Their Implications for Managing High-Risk Pig Farms.

African swine fever (ASF) is a highly contagious disease affecting domestic pigs and wild boars, with no effective vaccine or treatment available. In South Korea, extensive measures have been implemented to prevent ASF transmission between wild boars and ASF spillover from wild boars to pig farm sectors, including the search for ASF-infected carcasses in mountainous forests and the installation of fences across wide areas of these forests. To determine the priority search range for infected carcasses and establish pig farm-centered quarantine measures, it is necessary to predict the specific path of ASF outbreaks in wild boars and identify pig farms at high risk of ASF spillover from wild boars. Here, we aimed to predict suitable areas and geographical paths for ASF outbreaks in wild boars using the MaxEnt model and shortest-path betweenness centrality analysis. The analysis identified a high frequency of ASF outbreaks in areas with a suitability value ≥0.4 on the suitability map and in areas within a 1.8 km range from the path on the shortest-path map, indicating these areas were high-risk zones for ASF outbreaks. Among the 5063 pig farms analyzed, 37 were in the high-risk zone on the suitability map, 499 were in the high-risk zone on the shortest-path map, and 9 were in both risk zones. Of the 51 pig farm sectors with a dense distribution of pig farms (kernel density ≥ 8), 25 sectors were in contact with or partially overlapped the high risk zone on the suitability map, 18 sectors were located within the high risk zone on the shortest-path map, and 14 sectors were located within both risk zones. These findings aided in determining the priority range for searches for wild boar carcasses and enabled the establishment of preemptive ASF prevention measures around the pig farming sectors that are at risk of ASF spillover from wild boars.

Mouse Genome-Wide Association Study of Preclinical Group II Pulmonary Hypertension Identifies Epidermal Growth Factor Receptor.

Pulmonary hypertension (PH) is associated with features of obesity and metabolic syndrome that translate to the induction of PH by chronic high-fat diet (HFD) in some inbred mouse strains. We conducted a genome-wide association study (GWAS) to identify candidate genes associated with susceptibility to HFD-induced PH. Mice from 36 inbred and wild-derived strains were fed with regular diet or HFD for 20 weeks beginning at 6-12 weeks of age, after which right ventricular (RV) and left ventricular (LV) end-systolic pressure (ESP) and maximum pressure (MaxP) were measured by cardiac catheterization. We tested for association of RV MaxP and RV ESP and identified genomic regions enriched with nominal associations to both of these phenotypes. We excluded genomic regions if they were also associated with LV MaxP, LV ESP, or body weight. Genes within significant regions were scored based on the shortest-path betweenness centrality, a measure of network connectivity, of their human orthologs in a gene interaction network of human PH-related genes. WSB/EiJ, NON/ShiLtJ, and AKR/J mice had the largest increases in RV MaxP after high-fat feeding. Network-based scoring of GWAS candidates identified epidermal growth factor receptor (Egfr) as having the highest shortest-path betweenness centrality of GWAS candidates. Expression studies of lung homogenate showed that EGFR expression is increased in the AKR/J strain, which developed a significant increase in RV MaxP after high-fat feeding as compared with C57BL/6J, which did not. Our combined GWAS and network-based approach adds evidence for a role for Egfr in murine PH.

Weighted-spectral clustering algorithm for detecting community structures in complex networks

The community structure is a common non-trivial topological feature of many complex real-world networks. Existing methods for identifying the community structure are generally based on statistical-type properties, such as the degree of centrality, the shortest path betweenness centrality, the modularity, and so forth. However, the form of the community structure may vary widely, even if the number of vertices and edges are fixed. Consequently, it is difficult to be certain of the exact number of clusters within the network. Clustering schemes which require the number of clusters to be specified in advance often misjudge the community structure and yield a poor clustering performance as a result. Accordingly, the present study proposes a clustering algorithm, designated as the Weighted-Spectral Clustering Algorithm, capable of detecting the community structure of a network with no prior knowledge of the cluster number. The proposed method is tested on both computer-generated networks and several real-world networks for which the community structures are already known. The results confirm the ability of the proposed algorithm to partition the network into an appropriate number of clusters in every case.

Better Understanding Network of Electrical Terminal Stations by Topological Analysis

Preventing power transmission failures in its network of electrical terminal stations is a major concern for the infrastructure of the Thai capital Bangkok and its vicinity. Towards this objective the present study aims to analyse the network and its reliability under conditions of increased demand. The analysis is based on a representation of the network as a graph allowing to identify the most important terminal stations by graph-theoretical terms. These are, in particular, the centrality measures Degree Centrality (DC) giving the number of a station’s one-hop neighbours, Closeness Centrality (CC) describing the efficiency of power transmission from one station to others, shortest-path Betweenness Centrality (BC) indicating the number of a station’s occurrences on the shortest paths between indirectly connected stations, Hub describing stations that are connected to a large number of important stations, and Authority indicating the stations that connect many important stations. Experimental results revealed that the Bangkok Noi station was most significant when the measures DC, CC and BC were considered and, on the other hand, that the North Bangkok station was vital in terms of CC, Hub and Authority. Therefore, these stations need to be closely monitored and their operation to be carried out with extreme care in order to prevent the occurrence of power transmission failures within the Bangkok metropolitan area.

Topology manipulations for speeding betweenness centrality computation

We propose and evaluate two complementary heuristics to speed up exact computation of the shortest-path betweenness centrality (BC). Both heuristics are relatively simple adaptations of the standard algorithm for BC. Consequently, they generalize the computation of edge betweenness and most other variants, and can be used to further speed up betweenness estimation algorithms, as well. In the first heuristic, structurally equivalent vertices are contracted based on the observation that they have the same centrality and also contribute equally to the centrality of others. In the second heuristic, we first apply a linear-time betweenness algorithm on the block-cut-point tree and then compute the remaining contributions separately in each bi-connected component. Experiments on a variety of large graphs illustrate the efficiency and complementarity of our heuristics.

Augmented Betweenness Centrality for Environmentally Aware Traffic Monitoring in Transportation Networks

Network planning and traffic flow optimization require the acquisition and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements and so on. Data acquisition is an expensive process that involves household surveys and automatic as well as semiautomatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin–destination pair is required. As a result, problems that optimize nonlocal merit functions become highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this article we suggest a new traffic assignment model that is based on the concept of shortest path betweenness centrality measure, borrowed from the domain of complex network analysis. We show how betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high-resolution Israeli transportation data set derived from the analysis of cellular phones data. The group variant of the augmented betweenness centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.

On variants of shortest-path betweenness centrality and their generic computation

Betweenness centrality based on shortest paths is a standard measure of control utilized in numerous studies and implemented in all relevant software tools for network analysis. In this paper, a number of variants are reviewed, placed into context, and shown to be computable with simple variants of the algorithm commonly used for the standard case.