Betweenness Centrality Scores Research Articles

Temporal dynamics of microbial communities in the water of polyculture pond system for Chinese swimming crab Portunus trituberculatus

Microorganisms as the vital component of aquaculture ecosystems, were involved in nutrient cycling, water quality regulation, and the health of cultured species. In this study, aquatic water samples were collected monthly to reveal microbial community compositions, functions, and interactions in different habitats (pond and waterway) during the culture stages of the Chinese swimming crab Portunus trituberculatus in Shandong Province. Due to the semi-closed conditions with limited water exchange, the α diversities were lower in the polyculture pond. The relative abundances of the dominant phyla varied dramatically, and most biomarkers were identified in the polyculture pond. The β diversity of the microbial communities significantly differed among culture stages and habitats. Furthermore, the heterogeneity of prokaryotic microorganisms was greater than that of eukaryotic microorganisms. In the polyculture pond, the microbial community was sensitive to nutrient cycling, with abundant functions related to “photoautotrophy”, “nitrogen_respiration” and “nitrate_respiration”, corresponding to P. trituberculatus culture activities. The co-occurrence network of the microbial community in the polyculture pond tended to be looser than that in the waterway, with a lower number of taxa and correlations. In addition, these networks had nonrandom patterns and formed “small world” topological structures. The low-abundance genera with a higher level of betweenness centrality scores were the hubs or connectors in the networks and might play an essential role in microbial community composition. Furthermore, potential pathogens (Vibrio, Acholeplasma, Photobacterium, and Flavobacterium) deserve more attention during P. trituberculatus culture. This study will be useful for understanding microbial communities in polyculture pond systems and will provide a reference for the health aquaculture of P. trituberculatus.

Cocaine diminishes functional network robustness and destabilizes the energy landscape of neuronal activity in the medial prefrontal cortex.

We present analysis of neuronal activity recordings from a subset of neurons in the medial prefrontal cortex of rats before and after the administration of cocaine. Using an underlying modern Hopfield model as a description for the neuronal network, combined with a machine learning approach, we compute the underlying functional connectivity of the neuronal network. We find that the functional connectivity changes after the administration of cocaine with both functional-excitatory and functional-inhibitory neurons being affected. Using conventional network analysis, we find that the diameter of the graph, or the shortest length between the two most distant nodes, increases with cocaine, suggesting that the neuronal network is less robust. We also find that the betweenness centrality scores for several of the functional-excitatory and functional-inhibitory neurons decrease significantly, while other scores remain essentially unchanged, to also suggest that the neuronal network is less robust. Finally, we study the distribution of neuronal activity and relate it to energy to find that cocaine drives the neuronal network towards destabilization in the energy landscape of neuronal activation. While this destabilization is presumably temporary given one administration of cocaine, perhaps this initial destabilization indicates a transition towards a new stable state with repeated cocaine administration. However, such analyses are useful more generally to understand how neuronal networks respond to perturbations.

TransNeT-CGP: A cluster-based comorbid gene prioritization by integrating transcriptomics and network-topological features

The local disruptions caused by the genes of one disease can influence the pathways associated with the other diseases resulting in comorbidity. For gene therapies, it is necessary to prioritize the key genes that regulate common biological mechanisms to tackle the issues caused by overlapping diseases. This work proposes a clustering-based computational approach for prioritising the comorbid genes within the overlapping disease modules by analyzing Protein-Protein Interaction networks. For this, a sub-network with gene interactions of the disease pair was extracted from the interactome. The edge weights are assigned by combining the pairwise gene expression correlation and betweenness centrality scores. Further, a weighted graph clustering algorithm is applied and dominant nodes of high-density clusters are ranked based on clustering coefficients and neighborhood connectivity. Case studies based on neurodegenerative diseases such as Amyotrophic Lateral Sclerosis- Spinal Muscular Atrophy (ALS-SMA) pair and cancers such as Ovarian Carcinoma-Invasive Ductal Breast Carcinoma (OC-IDBC) pair were conducted to examine the efficacy of the proposed method. To identify the mechanistic role of top-ranked genes, we used Functional and Pathway enrichment analysis, connectivity analysis with leave-one-out (LOO) method, analysis of associated disease-related protein complexes, and prioritization tools such as TOPPGENE and Heml2.0. From pathway analysis, it was observed that the top 10 genes obtained using the proposed method were associated with 10 pathways in ALS-SMA comorbidity and 15 in the case of OC-IDBC, while that in similar methods like SAPDSB and S2B were 4, 6 respectively for ALS-SMA and 9, 10 respectively for OC-IDBC. In both case studies, 70 % of the disease-specific benchmark protein complexes were linked to top-ranked genes of the proposed method while that of SAPDSB and S2B were 55 % and 60 % respectively. Additionally, it was found that the removal of the top 10 genes disconnect the network into 14 distinct components in the case of ALS-SMA and 9 in the case of OC-IDBC. The experimental results shows that the proposed method can be effectively used for identifying key genes in comorbidity and can offer insights about the intricate molecular relationship driving comorbid diseases.

Oral rehydration solution (ORS) for fasting doping: Examining the Twitter data in Indonesia.

Oral rehydration solution (ORS) or oralit is a sugar and salt-based solution that restores electrolyte balance, counters dehydration and mitigates metabolic acidosis. In Indonesia, particularly during the month of Ramadan, the use of ORS as a form of fasting doping has become increasingly prevalent. This study aimed to analyze the patterns of communication, key influencers, and sentiment within the Twitter network in Indonesia regarding the use of ORS as fasting doping. From March 15 to March 26, 2023, Twitter data was collected using NodeXL software. The dataset was then analyzed using NodeXL and Gephi software to identify key influencers and patterns within the network. To assess attitudes towards the use of ORS as fasting doping expressed in tweets, sentiment analysis was conducted using Azure Machine. The dataset consisted of 13,746 tweets, from which the analysis revealed that Twitter discourse concerning the use of ORS as fasting doping demonstrated a diverse range of individuals. The top five users with the highest betweenness centrality scores were medical doctors, mention and confess (menfess) accounts, and personal accounts. The sentiment analysis of the collected tweets unveiled a relatively high negative sentiment toward the use of ORS for fasting purposes. Notably, the proportion of positive and neutral sentiments were comparable. Our data indicate that ORS use as fasting doping is controversial in Indonesia. Most tweets express concerns about misuse and negative consequences, indicating a need for guidance and regulation from public health authorities. Further research and guidelines are necessary to ensure the safe and appropriate use.

CB-SAGE: A novel centrality based graph neural network for floor plan classification

Graph Neural Networks (GNNs) have emerged as one of the most prominent research areas in accomplishing machine learning tasks over graphical networks. GNNs are prominently used in performing tasks like semi-supervised node classification, link prediction, and community detection. GraphSAGE is one of the most recent GNN models which is being used to accomplish these tasks. A floor plan is an architectural design of a building that represents various floor compartments. In this paper, we represent floor plan(s) as graph(s). We characterize floor room (compartment) classification as a node classification problem. We propose a variation of the traditional GraphSAGE (sample and aggregation) algorithm: Centrality based GraphSAGE (CB-SAGE), which captures the structural properties of the network. We use the average clustering coefficient and average betweenness centrality to capture structure properties. We compute CB scores for all the floor plan graphs. Top 70% nodes (based on CB scores) are selected for the training. During the training, we append the betweenness centrality score of each node as an additional feature in the feature matrix for the embedding process. We conduct experiments on the House-GAN dataset, which contains 1,43,184 vectorized floor plan images. The proposed method outperforms the current state-of-art models in accomplishing the task of floor plan classification. We compare our results with the traditional machine learning approach (MLP) and other GNN-based methods. Our approach achieves an accuracy of 96.70%, which is significantly (approximately 16%) higher than other state-of-the-art methods.

Soil nutrients, enzyme activities, and bacterial communities in varied plant communities in karst rocky desertification regions in Wushan County, Southwest China.

Vegetation restoration has become a common practice in karst rocky desertification (KRD) areas of southwestern China. The bacteria, which have made a connection between soil and plants, have been an important role in regulating the succession and restoration of karst vegetation. However, it is still unclear how soil bacterial communities and soil properties respond to natural vegetation restoration processes in karst areas. To address this gap, we investigated the soil nutrients, enzyme activity, and soil bacterial community among various plant communities, including farmland (FL), land with herbs only (SSI), herb-and-shrub land (SSII), woody thickets (SSIII), coniferous forest (SSIV), coniferous and broad-leaved mixed forest (SSV), and evergreen broad-leaved forest (SSVI). Our results found that SSII had the highest levels of soil organic matter, total nitrogen, available phosphorus, available nitrogen, sucrase, and β-glucosidase among all the plant communities. These results indicated that herb-and-shrub land have contributed to the rapid restoration of vegetation in KRD regions. FL exhibited the lowest levels of soil nutrients and enzyme activities, while showing the highest bacterial richness and diversity among all the plant communities. This suggested that appropriate human intervention can increase bacterial diversity and richness in the area. The predominant bacterial phylum also varied among the different plant communities, with Actinobacteria being the most abundant in SSI, SSII, SSIII, and SSIV, while Proteobacteria were the most abundant in SSV and SSVI. Furthermore, PCoA analysis demonstrated significant changes in the soil bacterial community structure, with SSI, SSII, SSIII, and SSIV had shared similar structures, while SSV and SSVI had comparable structures. As for soil characteristics, total phosphorus (TP) and total potassium (TK) were the primary factors affecting the soil bacterial community. SSV and SSVI had the most complex bacterial networks and were more stable than other groups. The genera Ktedonobacter, norank_f_Anaerolineaceae, and Vicinamibacter had the highest betweenness centrality scores and were identified as keystone genera in the co-occurrence network in KRD areas. In summary, our results have demonstrated that herb-and-shrub can promote community succession and increase soil nutrient levels in KRD regions.

The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis

BackgroundXBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant. ObjectivesThis study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA). MethodsThis experiment involved the collection of Twitter data through the keywords, “XBB.1.5″, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral. ResultsA total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely “ojimakohei”(red), mikito_777 (blue), “nagunagumomo” (green), “erictopol” (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and “ojimakohei” was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments. ConclusionJapan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.

C3P-RPL: A collaborative and proactive approach for optimal peer to peer path selection and sustenance

Internet of Things is evolving from information gathering platforms into collaborative systems wherein, smart devices actively interact with each other in a seamless manner. For instance, Internet of Robotic Things is envisioned to provide augmented solutions through collaboration of varied smart devices and robots. These visions revolve around the ability of smart devices to directly communicate and cooperate with each other in real time. In this context, this paper is an attempt to study RPL’s (Routing Protocol for Low-power Lossy Networks) point to point routing that creates multi-hop paths between peer nodes. This standard routing protocol is known for robust and failsafe upward paths but its peer to peer (P2P) routes are reported to be suboptimal. This work assesses P2P performance of RPL’s storing mode in a network of new generation devices having higher memory. Further, a Collaborative and Proactive Peer to Peer (C3P) path selection and sustenance approach is proposed where, root node collates incremental topology from collaborative nodes and disseminates optimal single source shortest path trees SPT(n). A progressive node betweenness centrality score ensures spread out paths. Minor topology changes are accommodated through incremental node and edge updates to targeted SPT(n) locally. Storing SPTs in intermediate nodes reduces storage and packet size. Through simulations and testbed experiments, it is proven that C3P-RPL improves simultaneous peer to peer communication between all the nodes. Specifically, the path length is reduced by 30% and subsequently the network latency drops by 65% in an experimental testbed of 47 nodes, making it suitable for collaborations.

Genetic diversity and community composition of arbuscular mycorrhizal fungi associated with root and rhizosphere soil of the pioneer plant Pueraria phaseoloides.

The pioneering plant Pueraria phaseoloides had a strong modulation effect on arbuscular mycorrhizal fungi (AMF) communities. Irrespective of geographical location, community composition of AMF in rhizosphere soil differed from that of the root. Co-occurrence network analysis revealed two AMF keystone species in rhizosphere soil (Acaulospora) and roots (Rhizophagus) of P. phaseoloides.

Impact of supervisors' research style on young biomedical scientists' capacity development as measured by REDi, a novel index of crossdisciplinarity.

The challenge for medical schools in Japan is to develop research activities for innovation. This study aimed at analyzing the connection between the research output of “promising researchers” (next-generation leaders in terms of research activity) and their supervisors' past research activities to identify those factors that impact researchers' performance. Activity was analyzed from the viewpoints of productivity, coauthorship networks, and research impact using a novel index called the Research Diversity Index (REDi) that quantifies crossdisciplinarity. Research funding, which has not yet been fully utilized in correlation studies of the characteristics of authors, was also considered in this study. For the promising researchers extracted using betweenness centrality scores within coauthorship networks, there were diachronic correlations between the records of the promising researchers and those of their supervisors. Supervisor leadership as measured by the number of last-authored publications and extent of networking had a positive effect on the promising researchers productivity. Supervisors' research style of integrating knowledge from multiple fields, as measured by REDi, was negatively correlated with the publication impact of promising researchers, suggesting that REDi is useful as a novel indicator of research quality not being captured by existing indices. It was also noted that establishing an academic presence through extensive collaborations could be advantageous for obtaining research funding, especially from top-down government programs. The possible implications of this study for promoting research activities are the importance of incorporating new doctorates into research groups at an early stage and that of promoting interinstitutional, crossdisciplinary collaborations.Classification codeMSC: 62P10 Applications of statistics to biology and medical sciences; meta-analysis.JEL: Z1Z10 Cultural Economics • Economic Sociology • Economic Anthropology- General.

An algorithm for updating betweenness centrality scores of all vertices in a graph upon deletion of a single edge

Abstract Betweenness centrality (BC) is a measure of the importance of a vertex in a graph, which is defined using the number of the shortest paths passing through the vertex. Brandes proposed an efficient algorithm for computing the BC scores of all vertices in a graph, which accumulates pair dependencies while traversing single-source shortest paths. Although this algorithm works well on static graphs, its direct application to dynamic graphs takes a huge amount of computation time because the BC scores must be computed from scratch every time the structure of graph changes. Therefore, various algorithms for updating the BC scores of all vertices have been developed so far. In this article, we propose a novel algorithm for updating the BC scores of all vertices in a graph upon deletion of a single edge. We also show the validity and efficiency of the proposed algorithm through theoretical analysis and experiments using various graphs obtained from synthetic and real networks.

Dynamics of Planktonic Microbial Community Associated with Saccharina japonica Seedling

Macroalgae interact with planktonic microbes in seawater. It remains unclear how planktonic microbes interact with the environment and each other during the cultivation processes of commercially important algal species. Such an interaction is important for developing environment-friendly mariculture methods. In this study, the dynamics of the planktonic microbial community associated with Saccharina japonica were profiled during the seedling production stage, with its environmental correlation and co-occurrence pattern determined simultaneously. Microbial richness increased and positively correlated with light intensity and contents of NO3− and PO43−. A clear temporal succession of the community was observed, which coincided with changes in light intensity, dissolved oxygen, pH, and NO3− content. α-Proteobacteria, Bacteroidetes, γ-Proteobacteria, and the genera prevalent in these taxa dominated the planktonic microbial community, and their relative abundance temporally changed. A profile of keystone taxa that is different from prevalent genera was identified based on betweenness centrality scores. A modularized co-occurrence pattern was determined, in addition to intensified species-to-species interactions at the core of the co-occurrence network. These findings expanded our cognization of the planktonic microbial community in response to S. japonica cultivation.

Disaster response network analysis in rural Temerloh, Pahang communities during the Malaysia 2020-2021 flood

Disaster risk reduction practices can be viewed as a collaborative environment managed by a diverse group of stakeholders including governments, private sectors and non-governmental organizations and research institutes as well as local communities. Insufficient collaboration and failure to coordinate across groups can lead to unsuccessful disaster recovery efforts. This study investigates the organizational roles and collaboration network among governmental and community organizations participating in Malaysia 2020-2021 flood response in rural Temerloh, Pahang. Social network analysis was conducted using Gephi open-source software to examine the general patterns of structures and the characteristics of the networks of stakeholders. News reports and organizational situation reports about the inter-organizational interaction and collaboration of stakeholders were identified using the manual coding analysis and analysed using Gephi, a social network analysis open-source software. The analysed results were ranked based on the categories of the centrality parameter, which highlights the extent of collaboration of key stakeholders in the network. The findings of this study indicate Malaysian Civil Defence (APM) and local government have high degree and betweenness centralities in the network. The number of private sectors active in disaster response was minimal, as were their centralities within the network, where they ranked last in every network measure. Rural communities and victims had lower betweenness centrality scores showed they had low network influence. NGOs are less involved in disaster response but are more involved in relief efforts such as cleaning muddy houses, recruiting medical and non-medical volunteers to help flood victims, distributing cleaning and healthcare supplies, and giving meals.

Investigation of gene expression and genetic simultaneous control associated with erectile dysfunction and diabetes.

Diabetes can cause some diseases or abnormalities. One of the disorders caused by diabetes may be erectile dysfunction (ED). ED is sexual dysfunction characterized by the inability to establish or maintain an erect penis during sexual activity and is a common problem of men with chronic type 2 diabetes. These processes, disorders and diseases are highly influenced by the genetics of individuals. In this study, the relationship between genes and diabetes and ED has been explored by a system biology approach. For this purpose, the samples from ten control and diabetic-ED rats were collected. After a search in Gene Expression Omnibus (GEO), series with accession numberGSE2457 comprising of 5 normal and 5 diabetic-ED rats were selected. Raw CEL files of these samples were normalized with robust multi-array average (RMA) expression measure method by using the linear models for microarray data (LIMMA) R package. The extracted probe IDs were transformed into 10451 unique and validated official gene symbols. Then, differentially expressed genes (DEGs) were identified between control and normal penile mucosa by employing the LIMMA R package. DEGs were classified by utilizing KEGG to underlying pathways by Enrichr. The expression values of DEGs were used to construct a gene regulatory network (GRN), by the GENEI3 R package. To analyze the topology of constructed GRNs, betweenness centrality was calculated. Genes with higher betweenness centrality scores were then identified, through the CytoNCA. We then took the commonality of DEGs genes and high-top ranking genes from CytoNCA via a predicted interaction network using GeneMANIAas the most likely important genes in erectile dysfunction. Among the 374 DEGs studied, 146 DEGs showed up-regulation and 228 DEGs displayed down-regulation expression in diabetic-ED rats. According to the Volcano plot, the dpp4, LOC102553868, Ndufa412, Oxct1, Atp2b3 and Zfp91 gene down-regulated and Lpl, Retsat, B4galt1 and Pdk4 genes up-regulated in ED and diabetic rats. Furthermore, genes like dpp4 acted as hubs in the inferred GRN.

Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models.

Bone is the most common site of distant metastasis from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Such bone metastases (BM) cause many painful skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with adverse effects on life quality. Many bone-targeting agents developed based on the current understanding of BM onset’s molecular mechanisms dull these adverse effects. However, only a few studies investigated potential predictors of high risk for developing BM, despite such knowledge being critical for early interventions to prevent or delay BM. This work proposes a computational network-based pipeline that incorporates a ML/DL component to predict BM development. Based on the proposed pipeline we constructed several machine learning models. The deep neural network (DNN) model exhibited the highest prediction accuracy (AUC of 92.11%) using the top 34 featured genes ranked by betweenness centrality scores. We further used an entirely separate, “external” TCGA dataset to evaluate the robustness of this DNN model and achieved sensitivity of 85%, specificity of 80%, positive predictive value of 78.10%, negative predictive value of 80%, and AUC of 85.78%. The result shows the models’ way of learning allowed it to zoom in on the featured genes that provide the added benefit of the model displaying generic capabilities, that is, to predict BM for samples from different primary sites. Furthermore, existing experimental evidence provides confidence that about 50% of the 34 hub genes have BM-related functionality, which suggests that these common genetic markers provide vital insight about BM drivers. These findings may prompt the transformation of such a method into an artificial intelligence (AI) diagnostic tool and direct us towards mechanisms that underlie metastasis to bone events.

ParTBC: Faster Estimation of Top- k Betweenness Centrality Vertices on GPU

Betweenness centrality (BC) is a popular centrality measure, based on shortest paths, used to quantify the importance of vertices in networks. It is used in a wide array of applications including social network analysis, community detection, clustering, biological network analysis, and several others. The state-of-the-art Brandes’ algorithm for computing BC has time complexities of and for unweighted and weighted graphs, respectively. Brandes’ algorithm has been successfully parallelized on multicore and manycore platforms. However, the computation of vertex BC continues to be time-consuming for large real-world graphs. Often, in practical applications, it suffices to identify the most important vertices in a network; that is, those having the highest BC values. Such applications demand only the top vertices in the network as per their BC values but do not demand their actual BC values. In such scenarios, not only is computing the BC of all the vertices unnecessary but also exact BC values need not be computed. In this work, we attempt to marry controlled approximations with parallelization to estimate the k -highest BC vertices faster, without having to compute the exact BC scores of the vertices. We present a host of techniques to determine the top- k vertices faster , with a small inaccuracy, by computing approximate BC scores of the vertices. Aiding our techniques is a novel vertex-renumbering scheme to make the graph layout more structured , which results in faster execution of parallel Brandes’ algorithm on GPU. Our experimental results, on a suite of real-world and synthetic graphs, show that our best performing technique computes the top- k vertices with an average speedup of 2.5× compared to the exact parallel Brandes’ algorithm on GPU, with an error of less than 6%. Our techniques also exhibit high precision and recall, both in excess of 94%.

Brain Topological Reorganization Associated with Visual Neglect After Stroke.

Background/Purpose: To identify brain hubs that are behaviorally relevant for neglect after stroke as well as to characterize their functional architecture of communication. Methods: Twenty acute right hemisphere damaged patients underwent neuropsychological and resting-state functional magnetic resonance imaging sessions. Spatial neglect was assessed by means of the Center of Cancellation on the Bells Cancellation Test. For each patient, resting-state functional connectivity matrices were derived by adopting a brain parcellation scheme consisting of 153 nodes. For every node, we extracted its betweenness centrality (BC) defined as the portion of all shortest paths in the connectome involving such node. Then, neglect hubs were identified as those regions showing a high correlation between their BC and neglect scores. Results: A first set of neglect hubs was identified in multiple systems including dorsal attention and ventral attention, default mode, and frontoparietal executive-control networks within the damaged hemisphere as well as in the posterior and anterior cingulate cortex. Such cortical regions exhibited a loss of BC and increased (i.e., less efficient) weighted shortest path length (WSPL) related to severe neglect. Conversely, a second group of neglect hubs found in visual and motor networks, in the undamaged hemisphere, exhibited a pathological increase of BC and reduction of WSPL associated with severe neglect. Conclusion: The topological reorganization of the brain in neglect patients might reflect a maladaptive shift in processing spatial information from higher level associative-control systems to lower level visual and sensory-motor processing areas after a right hemisphere lesion.

Interdicting International Drug Trafficking: a Network Approach for Coordinated and Targeted Interventions

There is a relative dearth of literature on both the effects of cross-border interdictions and the impact of different types of interventions on international drug trafficking. This study identifies the main trafficking routes for cocaine and heroin, along with comparing the disruptive effects induced by targeted and non-coordinated interventions. It adopts a social network approach to identify the routes along which cocaine and heroin are trafficked, and then simulates the impact of different interdiction strategies on these two trafficking networks. The findings indicate that targeting countries based on their respective positions in the networks, as opposed to on the basis of the quantity of drugs exchanged, is more likely to disrupt drug flows. More specifically, concentrating law enforcement resources on countries with several incoming or outgoing trafficking connections, or those countries that mediate between producer, transit and consumer countries, would appear to be particularly effective in this regard. Interventions focused on specific trafficking routes are also likely to be effective if these routes have high edge betweenness centrality scores. This study contributes to extant understanding on the vulnerability of cocaine and heroin international trafficking networks, and, moreover, demonstrates that empirically-driven strategies are potentially more effective at interdicting international trafficking than non-strategic and non-coordinated interventions.

Explaining the positional importance of actors involved in trafficking methamphetamine into Indonesia

ABSTRACT Disrupting drug operations requires a measured approach to identifying critical actors playing instrumental roles in support of illicit drug market activity. We use a quadratic assignment procedure (QAP) nodal regression routine to explore the explanatory relevance of human capital in accounting for an actor’s structural position within two methamphetamine trafficking communities—one originating from land-based smuggling and the other involving sea-based smuggling. We operationalise human capital with three dichotomous variables (occupying leadership roles, being involved in money laundering, or facilitating international smuggling) and capture positional importance with normalised degree and betweenness centrality scores. The results support arguments in favour of integrating centrality metrics with nodal attributes to improve targeted efforts to disrupt market activity. Additionally, network mapping must be sensitive to the local conditions surrounding specific types of drug trafficking operations—the structure of land-based and sea-based drug distribution chains supplying methamphetamine to Indonesian markets differ substantively. Limitations are discussed.

Forecasting Flight Delays Using Clustered Models Based on Airport Networks

Estimating flight delays is important for airlines, airports, and passengers, as the delays are among major costs in air transportation. Each delay may cause a further propagation of delays. Hence, the delay pattern of an airport and the location of the airport in the network can provide useful information for other airports. We address the problem of forecasting flight delays of an airport, utilizing the network information as well as the delay patterns of similar airports in the network. The proposed “Clustered Airport Modeling” (CAM) approach builds a representative time-series for each group of airports and fits a common model (e.g., REG-ARIMA) for each, using the network based features as regressors. The models are then applied individually to each airport data for predicting the airport's flight delays. We also performed a network based analysis of the airports and identified the Betweenness Centrality (BC) score as an effective feature in forecasting the flight delays. The experiments on flight data over seven years using 305 US airports show that CAM provides accurate forecasts of flight delays.