Bridging Centrality Research Articles

Complex networks after centrality-based attacks and defense

Exploration in complex networks has surged. Centrality measures play a pivotal role in pinpointing essential components within these networks. Previous work focus on nodes with the highest Betweenness centrality through extensive simulations. This paper analyzes the attack and/or defense strategy using one more centrality metric, bridging centrality and Bridging-Betweenness Fusion Attack (combination of both betweenness and bridging centrality). Our two-fold contribution is (1) Using high centrality removal as an attacking strategy and inspired by the dynamic node removal process, recalculated node method after each node removal is proposed. (2) In our defense techniques, new nodes are added to existing lower centrality nodes. They are added after attacks to restore the graph’s connectivity according to proposed defense strategies. Note that some attacks and defense techniques were already introduced while others are presented first time, e.g., the combination of two centrality measures for attack and a bridging-based defense strategy. This innovative approach presents a promising advancement in enhancing the resilience and fortification of complex networks against potential attacks, signifying a notable advantage of this work.

ICT Research networks and regional competitiveness: an analysis of the 7th Framework Program

ABSTRACT Using Social Network Analysis and the novel concept of bridging centrality this paper investigates the links generated by the research networks created by two of the European Union-funded Programs, the 7th Framework Program (FP7) and the ICT action of the Competitiveness and Innovation Framework Program (CIP) and their impacts on regional competitiveness. Our findings show a positive impact on the competitiveness of both networks. Those regions with higher capacities to establish bridging paths tend to show higher levels of competitiveness. Moreover, our results indicate that these effects differ between research and diffusion networks. Whereas the FP7-related network impacts all the dimensions of competitiveness, the CIP influences competitiveness only through the innovative channel. Our results highlight the importance of facilitating intra- and inter-regional collaboration networks between core and peripheral regions in Europe.

A Bridging Centrality Plugin for GEPHI and a Case Study for Mycobacterium Tuberculosis H37Rv.

Bridging Centrality (BriCe) is a popular measure that combines the Betweenness centrality and Bridging coefficient metrics to characterize nodes acting as a bridge among clusters. However, there were no implementations of the BriCe plugin that can be readily used in the GEPHI software or any other software dedicated to graph-based studies. In this paper, we present the BriCe plugin for GEPHI. It is available as a third-party functionality from the native GEPHI interface as a handy plugin to add; hence, no additional download and installation process is necessary. The BriCe plugin for GEPHI is open-source, and one can access the code through the GEPHI GitHub repository. As a use case of the BriCe plugin, we analyzed the genome of Mycobacterium tuberculosis H37Rv to identify biological explanations on why some proteins were ranked with top BriCe values? For instance, we were able to formulate a new hypothesis combining the predicted sub cellular localization and high BriCe values concerning lipopolysaccharides (LPS) exportation. Our hypothesis provides a possible link among proteins of a glycosyltransferase group and the type VII Secretion System. The Bridging Centrality plugin for GEPHI is an easy to use tool for analyzing complex graphs and draw novel insights from graphical data.

Exploring European-funded project-based networks in ICT and their links with regional total factor productivity: The FP7 and the CIP

AbstractThe research evaluation community has devoted multiple efforts to analyze the effects of Framework Programmes. However, there is little empirical evidence on their impact on economic performance. This article presents an empirical analysis of the research project-based networks created by 7th Framework Programme in the field of Information and Communication Technologies and investigates their links with regional total factor productivity. For comparison purposes, the diffusion network created by the Competitiveness and Innovation Framework Programme is also studied. Our results allow us to assess whether (or not) there are any common patterns in the research and diffusion links established by regions in these two European-supported networks and evaluate their (potentially) different connection with productivity. We use tools from Social Network Analysis and, specifically, the novel measure of bridging centrality, which takes into account territories’ internal microstructure.

Multi-Type Node Detection in Network Communities

Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc.

Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks

BackgroundDetection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes’ importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs.ResultsWe applied directed versions of centrality metrics to directed reaction-centric graphs of microbial metabolic networks. To investigate the local role of a node, we developed a novel metric, cascade number, considering how many nodes are closed off from information flow when a particular node is removed. High modularity and scale-freeness were found in the directed reaction-centric graphs and betweenness centrality tended to belong to densely connected modules. Cascade number and bridging centrality identified cascade subnetworks controlling local information flow and irreplaceable bridging nodes between functional modules, respectively. Reactions highly ranked with bridging centrality and cascade number tended to be essential, compared to reactions that other central metrics detected.ConclusionsWe demonstrate that cascade number and bridging centrality are useful to identify key reactions controlling local information flow in directed reaction-centric graphs of microbial metabolic networks. Knowledge about the local flow connectivity and connections between local modules will contribute to understand how metabolic pathways are assembled.

Bridging centrality as an indicator to measure the ‘bridging role’ of actors in networks: An application to the European Nanotechnology co-publication network

Abstract In the recent past, we can observe growing interest in STI studies in the notion of positioning indicators, shifting emphasis to actors in the innovation process and their R&D inter-linkages with other actors. In relation to this, we suggest a new approach for assessing the positioning of actors relying on the notion of bridging centrality (BC). Based on the concept of bridging paths, i.e. a set of two links connecting three actors across three different aggregate nodes (e.g. organisations, or regions), we argue that triangulation in networks is a key issue for knowledge recombinations and the extension of an actor’s knowledge base. As bridges are most often not empirically observable at the individual level of research teams, we propose an approximated BC measure that provides a flexible framework for dealing with the aggregation problem in positioning actors. Hereby, BC is viewed as a function of an aggregate node's (i) participation intensity in the network, (ii) its openness to other nodes (i.e. the relative outward orientation of network links), and iii) the diversification of links to other nodes. In addition, we propose a generalised version of the BC measure that accounts for different node categories. An illustrative example on the European Nanotechnology co-publication network observed at the level of organisations demonstrates the usefulness and complementary interpretation power in comparison to conventional centrality measures.

Centrality of regions in R&D networks: a new measurement approach using the concept of bridging paths

ABSTRACTCentrality of regions in R&D networks: a new measurement approach using the concept of bridging paths. Regional Studies. This paper introduces a novel measure of regional centrality in the context of research and development (R&D) networks. It first demonstrates some substantial problems of social network analysis (SNA)-based centrality measures to cope with regional R&D networks in a meaningful way. It then proposes a new measurement approach of regional network centrality based on the concept of interregional bridging paths (indirect connections at the regional level). The paper shows that the formal definition of the regional bridging centrality measure can be expressed in terms of three simple components: the participation intensity of a region in interregional R&D collaborations; its relative outward orientation in terms of all established links; and its diversification of R&D collaborations among partner regions. The measure and its behaviour with respect to other conventional centrality measures are illustrated by its application to the European co-patent network at the NUTS-2 level.

Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View.

The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0. Cholesterol, diacylglycreol, phosphatidylinositol-bis-phosphate, and inositol triphosphate were identified as core lipids that influence the signaling pathways in the LPPIN. RACα serine/threonine-protein kinase (AKT1) was a highly essential central protein. The gastrin-CREB pathway was greatly enriched; all enriched pathways in the LPPIN showed crosstalk with the phosphatidylinositol-3-kinase-Akt pathway, correlating with the central role of AKT1 in the network. The disease clusters identified in the LPPIN were cardiovascular disease, cancer, Alzheimer's disease, and Type II diabetes. In this context, we note that the commercially approved drug targets for hyperlipidemia in each disease cluster may potentially be repurposed for treatment of the specific disease. We report here top 10 potential drug targets that may mediate progression from hyperlipidemia to the respective disease state. ToppGene Suite was employed to identify candidates followed by a) discarding high closeness centrality nodes, and b) selecting nodes with high bridging centrality. Three potential targets could be mapped to specific disease clusters in the LPPIN. Lipids associated with acquired hyperlipidemia and each disease cluster identified may be useful as prognostic fingerprints. These findings provide an integrative view of lipid-protein interactions leading to acquired hyperlipidemia and the associated diseases, and might prove useful in future translational pharmaceutical research.

A novel paradigm for potential drug-targets discovery: quantifying relationships of enzymes and cascade interactions of neighboring biological processes to identify drug-targets

Target discovery is the most crucial step in a modern drug discovery development. Our objective in this study is to propose a novel paradigm for a better discrimination of drug-targets and non-drug-targets with minimum disruptive side-effects under a biological pathway context. We introduce a novel metric, namely, "pathway closeness centrality", for each gene that jointly considers the relationships of its neighboring enzymes and cross-talks of biological processes, to evaluate its probability of being a drug-target. This metric could distinguish drug-targets with non-drug-targets. Genes with lower pathway closeness centrality values are prone to play marginal roles in biological processes and have less lethality risk, but appear to have tissue-specific expressions. Compared with traditional metrics, our method outperforms degree, betweenness and bridging centrality under the human pathway context. Analysis of the existing top 20 drugs with the most disruptive side-effects indicates that pathway closeness centrality is an appropriate index to predict the probability of the occurrence of adverse pharmacological effects. Case studies in prostate cancer and type 2 diabetes mellitus indicate that the pathway closeness centrality metric could distinguish likely drug-targets well from human pathways. Thus, our method is a promising tool to aid target identification in drug discovery.

Bioinformatic Networks: Molecular Reticles for Pinpointing Pharmacological Target Selection

Bridging centrality was developed as a novel metric to capitalize on rich biological networks and enable selection of therapeutically efficient, druggable candidates. Through incorporation of local and global network properties, targetable nodes are identified that harbor latent capacity to intercept informational flow without compromising systemic functionality. Translational implementation holds potential for increased therapeutic specificity and offers opportunities to add value to drug discovery and development through facilitation of individualized treatment.

Identification of Information Flow-Modulating Drug Targets: A Novel Bridging Paradigm for Drug Discovery

Our objective in this study was to identify novel metrics for efficient identification of drug targets using biological network topology data. We developed a novel paradigm and metric, namely, bridging centrality, capable of identifying nodes critically involved in connecting or bridging modular subregions of a network. The topological and biological characteristics of bridging nodes were delineated in a diverse group of published yeast networks and in three human networks: those involved in cardiac arrest, C21-steroid hormone biosynthesis, and steroid biosynthesis. The bridging centrality metric was highly selective for bridging nodes. Bridging nodes differed distinctively from nodes with high degree and betweenness centrality. Bridging nodes had lower lethality, and their gene expression was consistent with independent regulation. Analysis of biological correlates indicated that bridging nodes are promising drug targets from the standpoints of efficacy and side effects. The bridging centrality method is a promising computational systems biology tool to aid target identification in drug discovery.