Global Centrality Research Articles

Identifying central nodes for information flow in social networks using compressive sensing

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse signal recovery. DICeNod can perform in a distributed manner by utilizing only local information at each node; thus, it is appropriate for large real-world and unknown networks. We theoretically show that by using only $$O \big ( k \log (\frac{n}{k}) \big )$$ indirect end-to-end measurements in DICeNod, one can recover top-k central nodes in a network with n nodes, even though the measurements have to follow network topological constraints. Furthermore, we show that the computationally efficient $$\ell _1$$ -minimization can provide recovery guarantees to infer such central nodes from the constructed measurement matrix with this number of measurements. Finally, we evaluate accuracy, speedup, and effectiveness of the proposed method by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that the number of correctly identified central nodes and their estimated rank based on the DICeNod framework and the global betweenness centrality correlate very well. Moreover, the results show that DICeNod is a scalable framework and it can be efficiently used for identifying central nodes in real-world networks.

F152. N-ACETYL-CYSTEINE SUPPLEMENTATION IMPROVES FUNCTIONAL CONNECTIVITY IN THE CINGULATE CORTEX IN EARLY PSYCHOSIS

BackgroundIn schizophrenic patients, increasing evidence suggests that oxidative stress is involved on the disease pathophysiology. Estimation of the level of glutathione (GSH), main actor of the brain redox dysregulation, has revealed a decreased GSH levels in early psychosis patients (EPP). N-Acetyl-Cysteine (NAC) is an antioxidant and precursor of GSH, almost devoid of side effects. In chronic patients, add-on of NAC improved negative symptoms and reduced side effects of antipsychotics. In a recent double-blind randomized placebo-controlled trial, early psychosis patients received NAC supplementation. Whether NAC treatment leads to improvement in FC in EPP is unknown. Given the known connectivity disruptions in schizophrenia and given that GSH levels correlated with FC within the cingulate cortex, we investigate the effect of NAC supplementation during 6 months in EPP on cingulum cortex FC.Methods20 EPP which constitutes a subgroup of the double-blind randomized placebo-controlled trial by [7] were scanned. T1-weighted volumes (1 mm in-plane, 1.2 mm slice thickness, TR/TE/TI 2300/2.98/900ms) and resting-state fMRI (gradient-echo-EPI sequence, 3.3x3.3x3.3 mm3, TR/TE 1920/30ms) recording were acquired on a Siemens-3T scanner in EPP who received either NAC (n=9) or placebo (n=11) as add-on treatment at baseline (T1) and follow-up (T2) after 6 months. A control group (n=93) was scanned in the same conditions. Data were processed with CMTK using a 68 regions parcellation according to a state-of-the-art pipeline. Time series were averaged over the Freesurfer regions and Pearson’s correlation was used as FC measure. We investigated FC changes occurring in the cingular cortices between T1 and T2 for NAC and placebo EPP and between EPP and CTRL by looking at the FC strength of these regions. Moreover, we assessed global efficiency and edge betweenness centrality (BC) for the whole brain network.ResultsFC between caudal and isthmus cingulate cortices increases significantly after 6 months for patients who received NAC supplementation (p=0.01) but not for patients who received the placebo. FC of these regions was also higher in patients after 6 months of NAC supplementation (but not in placebo or baseline patients). Regarding the local network measures, BC differences are observed for the same regions. Indeed, edge BC increases between caudal and isthmus cingulate regions after NAC supplementation compared to placebo (p=0.015). At the nodal level, the increase of BC can be mainly ascribed to the isthmus cingulate cortex (p=0.01).DiscussionNAC supplementation has an impact on FC between caudal and isthmus cingulate regions in our dataset. NAC increases FC strength between these two regions. This strength is significantly higher than for matched control subjects. NAC has a stimulating effect on FC in regions along the cingulum bundle. This FC change could be partially explained by the changes in BC. The edge BC of the caudal and isthmus cingulate connection is increased for NAC subjects: a highest number of shortest paths go through this connection. It shows a growing importance of this edge in connecting the frontal and posterior regions of the brain network. A higher number of subjects would allow to confirming the robustness of these findings. This is a first study assessing the impact of NAC treatment on FC in a sample of EPP. After 6 months, NAC improves FC in caudal anterior and isthmus cingulate cortices. This FC increase is characterized by a higher BC in these brain areas, improving the connection between the frontal and posterior regions along the cingulum bundle. These results suggest that FC along the cingulum may be a biomarker for NAC treatment response.

Numerically approximating centrality for graph ranking guarantees

Many real-world datasets can be represented as graphs. Using iterative solvers to approximate graph centrality measures allows us to obtain a ranking vector on the nodes of the graph, consisting of a number for each vertex in the graph identifying its relative importance. In this work the centrality measures we use are Katz Centrality and PageRank. Given an approximate solution, we use the residual to accurately estimate how much of the ranking matches the ranking given by the exact solution. Using probabilistic matrix norms, we obtain bounds on the accuracy of the approximation compared to the exact solution with respect to the highly ranked nodes and apply numerical analysis to the computation of centrality with iterative methods. This relates the numerical accuracy of the linear solver to the data analysis accuracy of finding the correct ranking. In particular, we answer the question of which pairwise rankings are reliable given an approximate solution to the linear system. Experiments on many real-world undirected and directed networks up to several million vertices and several hundred million edges validate our theory and show that we are able to accurately estimate large portions of the approximation. We also analyze the difference between global centrality scores and personalized scores (w.r.t. specific seed vertices). By analyzing convergence error, we develop confidence in the ranking schemes of data mining. We show we are able to accurately guarantee ranking of vertices with an approximation to centrality metrics faster than current methods.

Functional Role and Hierarchy of the Intermolecular Interactions in Binding of Protein Kinase Clients to the Hsp90-Cdc37 Chaperone: Structure-Based Network Modeling of Allosteric Regulation.

A fundamental role of the Hsp90-Cdc37 chaperone machinery in mediating conformational development and activation of diverse protein kinase clients is essential for signal transduction. Structural and biochemical studies have demonstrated that characterization of global conformational changes and allosteric interactions in the Hsp90-Cdc37-kinase complexes are central to our understanding of the mechanisms underlying kinase recruitment and processing by the Hsp90-Cdc37 chaperone. The recent cryo-electron microscopy structure of the Hsp90-Cdc37-Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have characterized functional role and hierarchy of the intermolecular interactions in binding of protein kinase clients to the Hsp90-Cdc37 system. The network analysis revealed important relationships between structural stability, global centrality, and functional significance of regulatory hotspots in chaperone regulation and client recognition. A unique asymmetric topography of the intermolecular communities in the chaperone-kinase complex has quantified a central mediating role of Cdc37 in client recognition and allosteric regulation of the chaperone-kinase complex. Modeling of allosteric pathways in the chaperone complex has further clarified structural and energetic signatures of allosteric hotspots, particularly linking sites of post-translational modifications in Hsp90 with their role in allosteric interactions and client regulation. The results of this integrative computational study are compared with a wide range of structural, biochemical, and cell-based experiments, offering a robust network-centric model of allosteric regulation and client kinase recognition by the Hsp90-Cdc37 chaperone machine.

The reorganization of functional architecture in the early-stages of Parkinson's disease

IntroductionThe study aim was to identify longitudinal abnormalities of functional connectivity and its relation with motor disability in early to moderately advanced stages of Parkinson's disease patients. Methods3.0T structural and resting-state functional MRI was performed in healthy subjects (n = 16) and Parkinson's disease patients (n = 16) with mean disease duration of 2.2 ± 1.2 years at baseline with a clinical follow-up of 1.5 ± 0.3 years. Resting-state fMRI analysis included region-to-region connectivity in correlation with UPDRS-III scores and computation of Global Efficiency and Degree Centrality. ResultsAt baseline, patients' connectivity increased between the cerebellum and somatomotor network, and decreased between motor regions (Rolandic operculum, precentral gyrus, supplementary motor area, postcentral gyrus) and cingulate connectivity. At 1.5 years follow-up, connectivity remained altered in the same regions identified at baseline. The cerebellum showed additional hyperconnectivity within itself and to the caudate nucleus, thalamus and amygdala compared to controls. These differences correlated with UPDRS-III scores. Seed-based connectivity revealed increased involvement of the default mode network with precentral gyrus in patients at follow-up investigation. ConclusionResting-state fMRI identified marked disturbances of the overall architecture of connectivity in Parkinson's disease. The noted alterations in cortical motor areas were associated with cerebellar hyperconnectivity in early to moderately advanced stages of Parkinson's disease suggesting ongoing attempts of recovery and compensatory mechanism for affected functions. The potential to identify connectivity alterations in regions related to both motor and attentional functions requires further evaluation as an objective marker to monitor disease progression, and medical, as well as surgical interventions.

"China and Japan and their Region"

"China and Japan and their Region" Edward Friedman (bio) Ezra F. Vogel. China and Japan: Facing History. Cambridge, MA: The Belknap Press of Harvard University Press, 2019. x, 523 pp. Hardcover $39.95. isbn 978-0-674-91657-9. Regions differ. There are more than twice as many Vietnamese as all the people of the entire Caribbean. China attempting to dominate a maritime Asia full of major powers such as Japan therefore is not the same as America in the early twentieth century dominating the Caribbean where little Haiti, Cuba, and the Dominican Republic are the biggest states. Japanese national identity, according to June Dreyer's brilliant and illuminating history of China–Japan relations,1 requires Japan to preserve its dignity and independence by not submitting to subordination to China. Japan, the world's third largest economy, actually, wants more. It seeks to rival China for influence in the region. In contrast, no Caribbean nation can even imagine rivaling the United States. Yet Chinese Communist Party (CCP) leaders, wishing to copy America's global rise, imagine regional dominance, as with America and the Caribbean, as the prelude to the CCP state's global centrality. A key question then is what happens when regional nations, for example, Pakistan versus India, Ukraine versus Russia, Tonga and Fiji versus Australia, India, Indonesia, and Vietnam versus China, or even little Cuba versus the superpower United States, resist a would-be regional hegemon, in our case, Japan versus China. Regional resistance to a would-be hegemon is pervasive. It would be a catastrophe if the CCP state's attempt to dominate Japan and the region led to a major military conflict. Ezra Vogel's book is a well-written and well-informed effort to contribute to the noble purpose of avoiding such a regional war. He offers, in place of a history where conflict pervades the relationship, a narrative in which the two people have done best when they have cooperated with each other, as "from 600 to 838 when Japan borrowed . . . from China, and from 1905 to 1937 and 1978 through the 1990's, when China borrowed from Japan" (p. 412). Vogel vividly describes these three periods as well as the long history of China and Japan in general. His important book is also a wonderful read. Vogel is surely correct that the national narrative of China's chauvinistic rulers omits times of China–Japan cooperation. Yet, even Vogel ignores some periods of Japan's cooperation with Chinese. In the Meiji era, Sun Yat-sen's anti-Manchu rebels cooperated with Japan at a time when the Qing Empire of Manchu militarism was aligned with an expansionist Tsarist Russia. China joining with Russia worried Japanese ruling groups. They sought a partner in [End Page 228] Sun Yat-sen. Another China/Japan entente that Vogel does not discuss was a Mao era quest for cooperation with democratic Japan from 1971 to 1981 as part of a global CCP united front policy against Soviet Russian expansionism. In these two instances of cooperation of Japan with Sun and Mao, the political source of alignment was a perceived need of both sides to join against a common threat, Russia. But, military superpower Soviet Russia imploded in 1991, leaving behind a much-weakened Russia. The CCP state's military then could change its focus from a now militarily secure continental Asia, no longer occupied by USSR troops, to maritime Asia. It could promote the People's Republic of China (PRC) maritime expansion. Whatever Japan's continuing problems with Russia occupying Japan's four northernmost islands, the immediate threat to Japan's territorial integrity became an ambitious CCP state. In the post-Soviet era, Russia and China became partners. There no longer was a common Russian enemy of both Beijing and Tokyo to provide a solid basis for deep cooperation and peace between China and Japan. This is not a lesson that Vogel draws from his magnificent study of China–Japan relations. Regions change. China–Japan relations require contextualization. An imperial era Confucian East Asia of China, Japan, Korea, and Vietnam is without substance in an age where market imperatives and technological capabilities place China and Japan in the Indo...

Global network centrality of university rankings.

Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport’s aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

A Novel Analysis Method of Geographical Centrality Based on Space of Flows

Geographical centrality is an evolving concept that differs from one perspective to another at different stages. The unprecedented development of high-speed information and transportation networks has highlighted the important role of space of flows and has restructured the mode of spatial interaction. The geographical centrality analysis method based on relational networks currently becomes the mainstream, but most related methods ignore the spatial structure. In this study, we first analyze the impacts of space of flows on geographical space based on spatial interaction theory. We argue that geographical space and space of flows dominate short- and long-distance interactions, respectively. Based on this hypothesis, the concept of geographical centrality based on space of flows is proposed. The new concept categorizes spatial units into four types: global centers, isolated units, externally oriented units, and locally oriented units. Then, two quantitative measures, namely, global and local geographical centrality indexes, are defined. In the case study, we analyze the geographical centrality of cities in China at three different spatial scales and compare the result with three other geographical centrality analysis methods. City attribute is concluded to be more important than spatial distance in urban spatial interaction at the national scale, and this situation is caused by the effect of space of flows on geographical space. The similarities and differences between the proposed geographical centrality analysis method and the classic spatial autocorrelation analysis method of Moran’s I are also discussed.

ANALISIS STRUKTUR JARINGAN KOMUNIKASI DALAM ADAPTASI EKONOMI, SOSIAL DAN BUDAYA PADA PAGUYUBAN BABUL AKHIRAT DI KOTA BATAM

<p>ABSTRACT</p><p><br />The purposes of this study include : (1) To know the communication network on the Babul Akhirat <br />community in the economic, social, and cultural adaptation; (2) To identify individual roles in the<br />communication network of the Babul Akhirat community in the economic, social, and cultural<br />adaptation; and (3) To describe the communication network centrality of the Babul Akhirat community <br />in the economic, social, and cultural adaptation. This study constitutes a descriptive research with <br />the quantitative approach. The study design of communication network is a complete network and<br />the focus of this study was at the system level. In this study, the researcher referred to the nominalist<br />approach by viewing the study population. The actor determination in this study was based on the<br />positional strategy, namely the Babul Akhirat Community in Batam City where the actors becoming<br />samples of the population were members of the Babul Akhirat community. Sampling used in this study<br />was the samples of small groups, that were all members of the Babul Akhirat community, amounted to<br />82 members. This study used the analysis technique of sociometric data and the communication network<br />structures by using by using UCINET VI. The results of this study showed: (1) The social and economic<br />communication network took possession of the interlocking network structure; (2) The individual roles<br />in the communication network of the Babul Akhirat community in the economic, social, and cultural<br />adaptation encompassed opinion leader, cosmopolite, and bridge; and (3) The actors representing the<br />highest local centrality value for the communication network of economic adaptation were node 15, 22,<br />30, and 59, while values of the communication network for the social adaptation were node 22 and 31,<br />and values of the network communication for the cultural adaptation were node 30, 47, and 45. On the<br />other result, actors representing the lowest global centrality value for each communication network of<br />economic adaptation, social adaptation, and cultural adaptation were node 23, node 5, and node 13. <br />Keywords: Communication Network Structure, Actor, Babul Akhirat Community</p>

Target recovery in complex networks

The invulnerability of complex networks is an important issue which has been widely analyzed in different fields. A lot of works have been done to measure and improve the stability of complex networks when being attacked. Recently, how to recover networks after attack was intensively studied. The existing methods are mainly designed to recover the overall functionality of networks, yet in many real cases the recovery of important nodes should be given priority, to which we refer target recovery. For example, when the cold wave paralyses the railway networks, target recovery means to repair those stations or railways such that the transport capacity of densely-populated cities can be recovered as fast as possible. In this paper, we first compare the impact of attacks on the whole network and target nodes respectively, and then study the efficiency of traditional recovery methods that are proposed based on global centrality metrics. Furthermore, based on target centrality metrics, we introduce a local betweenness recovery method and we find it has better performance than the traditional methods. We finally propose a hybrid recovery method which includes local betweenness metric and local closeness metric. The performance of the hybrid method is shown to be similar to that of the greedy algorithm.

Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us?

ObjectiveTraumatic brain injury (TBI) is associated with cognitive and motor deficits, and poses a significant personal, societal, and economic burden. One mechanism by which TBI is thought to affect cognition and behavior is through changes in functional connectivity. Graph theory is a powerful framework for quantifying topological features of neuroimaging-derived functional networks. The objective of this paper is to review studies examining functional connectivity in TBI with an emphasis on graph theoretical analysis that is proving to be valuable in uncovering network abnormalities in this condition. MethodsWe review studies that have examined TBI-related alterations in different properties of the functional brain network, including global integration, segregation, centrality and resilience. We focus on functional data using task-related fMRI or resting-state fMRI in patients with TBI of different severity and recovery phase, and consider how graph metrics may inform rehabilitation and enhance efficacy. Moreover, we outline some methodological challenges associated with the examination of functional connectivity in patients with brain injury, including the sample size, parcellation scheme used, node definition and subgroup analyses. ResultsThe findings suggest that TBI is associated with hyperconnectivity and a suboptimal global integration, characterized by increased connectivity degree and strength and reduced efficiency of functional networks. This altered functional connectivity, also evident in other clinical populations, is attributable to diffuse white matter pathology and reductions in gray and white matter volume. These functional alterations are implicated in post-concussional symptoms, posttraumatic stress and neurocognitive dysfunction after TBI. Finally, the effects of focal lesions have been found to depend critically on topological position and their role in the network. ConclusionGraph theory is a unique and powerful tool for exploring functional connectivity in brain-injured patients. One limitation is that its results do not provide specific measures about the biophysical mechanism underlying TBI. Continued work in this field will hopefully see graph metrics used as biomarkers to provide more accurate diagnosis and help guide treatment at the individual patient level.

Network centrality and funding rates in the e-MID interbank market

This paper empirically investigates the role of banks’ network centrality in the interbank market on their funding rates. Specifically we analyze transaction data from the e-MID market, the only electronic interbank market in the Euro Area and US, over the period 2006–2009 that encompasses the global financial crisis. We show that interbank spreads are significantly affected by both local and global measures of connectedness. The effects of network centrality increased as the financial crisis evolved. Local measures show that having more links increases borrowing costs for borrowers and reduces premia for lenders. For global network centrality, borrowers receive a significant discount if they increase their intermediation activity and become more central, while lenders pay in general a premium (i.e. receive lower rates) for centrality. This provides evidence of the ‘too-interconnected-to-fail’ hypothesis.

Key species and impact of fishery through food web analysis: A case study from Baja California Sur, Mexico

Ecosystem-Based Management (EBM) aims to support the protection of natural ecosystems and to improve economic activities. It requires considering all of the actors interacting in social-ecological systems (e.g., fish and fishers) in the understanding that their interplay determines the dynamic behavior of the single actors as well as that of the system as a whole. Connections are thus central to EBM. Within the ecological dimension of socio-ecological systems, interactions between species define such connections. Understanding how connections affect ecosystem and species dynamics is often impaired by a lack of data. We propose food web network analysis as a tool to help bridge the gap between EBM theory and practice in data-poor contexts, and illustrate this approach through its application to a coastal marine ecosystem in Baja California Sur, Mexico. First, we calculated centrality indices to identify which key (i.e., most central) species must be considered when designing strategies for sustainable resource management. Second, we analyzed the resilience of the system by measuring changes in food web structure due to the local extinction of vulnerable species (i.e., by mimicking the possible effect of excessive fishing pressure). The consequences of species removals were quantified in terms of impacts on global structural indices and species' centrality indices. Overall, we found that this coastal ecosystem shows high resilience to species loss. We identified species (e.g., Octopus sp. and the kelp bass, Paralabrax clathratus) whose protection could further decrease the risk of potential negative impacts of fishing activities on the Baja California Sur food web. This work introduces an approach that can be applied to other ecosystems to aid the implementation of EBM in data-poor contexts.

Is Poverty Decentralizing? Quantifying Uncertainty in the Decentralization of Urban Poverty

In this article we argue that the recent focus on the suburbanization of poverty is problematic because of the ambiguities and inconsistencies in defining suburbia. To improve transparency, replicability, and comparability, we suggest that research on the geographical changes to the distribution of poverty should focus on three questions: (1) How centralized is urban poverty? (2) To what extent is it decentralizing? (3) Is it becoming spatially dispersed? With respect to all three questions, the issue of quantifying uncertainty has been underresearched. The main contribution of the article is to provide a practical and robust solution to the problem of inference based on a Bayesian multivariate conditional autoregressive (CAR) model, made accessible via the R software package CARBayes. Our approach can be applied to spatiotemporally autocorrelated data and can estimate both levels of and change in global relative centralization index (RCI), local RCIs, and dissimilarity indexes. We illustrate our method with an application to Scotland's four largest cities. Our results show that poverty was centralized in 2011 in Glasgow, Dundee, and Aberdeen. Poverty in Edinburgh, however, was decentralized: Nonpoor households tend to live closer to the center than poor ones and increasingly so. We also find evidence of statistically significant reductions in centralization of poverty in all four cities. To test whether this change is associated with poverty becoming more dispersed, we estimate changes to evenness and local decentralization of poverty, revealing complex patterns of change.

Coupling centrality and authority of co-processing model on complex networks

Coupling centrality and authority of co-processing model on complex networks are investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are obtained to disclose the formation. Considering the influence of a node to the global dynamical behavior, coupling centrality and authority are introduced for each node, which determine the relative importance and authority of nodes in the diffusion process. Furthermore, the experimental results on large-scale complex networks confirm our analytical prediction.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing.

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Centrality measures for networks with community structure

Understanding the network structure, and finding out the influential nodes is a challenging issue in large networks. Identifying the most influential nodes in a network can be useful in many applications like immunization of nodes in case of epidemic spreading, during intentional attacks on complex networks. A lot of research is being done to devise centrality measures which could efficiently identify the most influential nodes in a network. There are two major approaches to this problem: On one hand, deterministic strategies that exploit knowledge about the overall network topology, while on the other end, random strategies are completely agnostic about the network structure. Centrality measures that can deal with a limited knowledge of the network structure are of prime importance. Indeed, in practice, information about the global structure of the overall network is rarely available or hard to acquire. Even if available, the structure of the network might be too large that it is too much computationally expensive to calculate global centrality measures. To that end, a centrality measure is proposed here that requires information only at the community level. Indeed, most of the real-world networks exhibit a community structure that can be exploited efficiently to discover the influential nodes. We performed a comparative evaluation of prominent global deterministic strategies together with stochastic strategies, an available and the proposed deterministic community-based strategy. Effectiveness of the proposed method is evaluated by performing experiments on synthetic and real-world networks with community structure in the case of immunization of nodes for epidemic control.

RIM: Relative-importance based data forwarding in people-centric networks

The fast penetration of mobile phones has arisen the requirement to share content (e.g., news, photo, music, video clips, etc.) among devices. To improve the efficiency of content sharing, recent works select nodes with high centrality in the system to cache and forward contents, resulting in a bias towards the most popular nodes. However, these nodes are not the appropriate candidates for target nodes, since the globally powerful nodes may have little influence on some local communities where the targets belong. Interestingly, we observe that nodes with low global centrality but high relative importance to the targets bear most weight on content allocation. Motivated by this observation, we exploit the relative importance of a node with respect to a group of nodes to guide the allocation process. We quantify the relative importance of nodes using graph spectrum theory, we then propose RIM (Relative IMportance), a novel data forwarding scheme to improve the allocation efficiency. By applying RIM on three real people-centric scenarios, the evaluation results show that RIM achieves significantly better mean delivery delay and cost than the state-of-the-art solutions, while achieving delivery ratios sufficiently close to those by Epidemic under different message TTL requirements.

Human-mobility networks, country income, and labor productivity

AbstractThis paper asks whether the level of integration of world countries in the international network of temporary human mobility can explain differences in their per-capita income and labor productivity. We disentangle the role played by global country centrality in the network from traditional openness measures, which only account for local, nearest-neighbor linkages through which ideas and knowledge can flow. Using 1995-2010 data, we show that global country centrality in the international temporary human-mobility network enhances both per-capita income and labor productivity. Our results hold cross-sectionally, as well as in a dynamic-panel estimation, and take into account potential endogeneity issues. Our findings imply that how close a country is to the theoretical technological frontier, depends not only on how much she is open to temporary human mobility, but mostly on whether she is embedded in a web of relationships connecting her with other influential partners in the network. Our exercises also suggest that most of the gain in income and productivity can be attained if country centrality in the network comes mostly from influential partners that lie not too far away from, but neither too close to them in the network.

Measures of node centrality in mobile social networks

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.