Showing papers in "Network Science in 2013"
TL;DR: The first few pages of the first issue of Network Science, the new journal created because network science is exploding, share their own vision of the emerging science of networks.
Abstract: This is the beginning of Network Science. The journal has been created because network science is exploding. As is typical for a field in formation, the discussions about its scope, contents, and foundations are intense. On these first few pages of the first issue of our new journal, we would like to share our own vision of the emerging science of networks.
1,238 citations
TL;DR: To adapt Dijkstra’s algorithm for quantum repeater networks that generate entangled Bell pairs, the key differences are quantified and a link cost metric is defined, seconds per Bell pair of a particular fidelity, where a single Bell pair is the resource consumed to perform one quantum teleportation.
Abstract: Quantum networks will support long-distance quantum key distribution (QKD) and distributed quantum computation, and are an active area of both experimental and theoretical research. Here, we present an analysis of topologically complex networks of quantum repeaters composed of heterogeneous links. Quantum networks have fundamental behavioral differences from classical networks; the delicacy of quantum states makes a practical path selection algorithm imperative, but classical notions of resource utilization are not directly applicable, rendering known path selection mechanisms inadequate. To adapt Dijkstra’s algorithm for quantum repeater networks that generate entangled Bell pairs, we quantify the key differences and define a link cost metric, seconds per Bell pair of a particular fidelity, where a single Bell pair is the resource consumed to perform one quantum teleportation. Simulations that include both the physical interactions and the extensive classical messaging confirm that Dijkstra’s algorithm works well in a quantum context. Simulating about three hundred heterogeneous paths, comparing our path cost and the total work along the path gives a coefficient of determination of 0.88 or better.
125 citations
TL;DR: This work combines a gravity model specification with “latent space” networks to develop a dynamic mixture model for real-valued directed graphs that substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics.
Abstract: The gravity model, long the empirical workhorse for modeling international trade, ignores network dependencies in bilateral trade data, instead assuming that dyadic trade is independent, conditional on a hierarchy of covariates over country, time, and dyad. We argue that there are theoretical as well as empirical reasons to expect network dependencies in international trade. Consequently, standard gravity models are empirically inadequate. We combine a gravity model specification with “latent space” networks to develop a dynamic mixture model for real-valued directed graphs. The model simultaneously incorporates network dependencies in both trade incidence and trade volumes. We estimate this model using bilateral trade data from 1990 to 2008. The model substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics. We illustrate the model's usefulness by tracking trading propensities between the USA and China.
109 citations
TL;DR: Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity, which suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function.
Abstract: Keywords: connectome ; community structure ; dynamics ; Markov process ; resting - state ; LTS5 Reference EPFL-ARTICLE-185801doi:10.1017/nws.2013.19 URL: http://arxiv.org/abs/1304.0485 Record created on 2013-04-03, modified on 2017-05-10
107 citations
TL;DR: In this article, the authors use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions.
Abstract: We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily. Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding). Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.
106 citations
TL;DR: In this article, a large collection of such networks representing friendships among students at US high and junior high schools was analyzed and it was found that almost all unreciprocated friendships consist of a lower ranked individual claiming friendship with a higher ranked one.
Abstract: In empirical studies of friendship networks, participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower ranked individual claiming friendship with a higher ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.
86 citations
TL;DR: How do co-voting similarity networks in the US Senate and individual careers over time reveal positional mobility over time?
Abstract: How typical is the partisanship of current American politics and how do politicalactors navigate divided networks?To find out, we measure co-voting similarity networks in the US Senate andtrace individual careers over time. Standard network visualization tools fail ondense highly clustered networks, so we used two aggregation strategies to clarifypositional mobility over time. First, clusters of Senators who often vote the sameway capture coalitions, and allow us to measure polarization quantitatively throughmodularity (Newman, 2006; Waugh et al., 2009; Poole, 2012). Second, we use role-based blockmodels (White et al., 1976) to identify role positions, identifying setsof Senators with highly similar tie patterns. Our partitioning threshold for rolesis stringent, generating many roles occupied by single Senators. This combinationallows us to identify movement between positions over time (specifically, we used theKernighan–Lin improvement of a Louvain method greedy partitioning algorithmfor modularity [Blondel et al., 2008], and CONCOR with an internal similaritythreshold for roles; see Supplementary materials for details).The figure maps this dynamic coalition network, one two-year Congress at a time.Nodes indicate structurally equivalent positions, scaled by number of Senators andshaded by their voter agreement level. In each period there are two “party loyalist”positions, anchored on the y-axis proportional to the modularity score. The y-position of other nodes—usually individuals—is based on the balance of their votesrelative to these anchors. Positions are linked over time by identity arcs connectingeach person to themselves over time, labeled to trace individual careers (the widthsof arcs between aggregate positions indicate the number of people moving betweenthem over time).Substantively, we have not seen the current level of partisanship (by this measure)sincetheearly1900s,begging thequestionwhetherthisrepresentsaqualitativephaseshiftinthestructureofSenatepolitics.Foralongperiod(atleastfrom1932to1992),it appears that Senators could occupy mixed-party roles in multiple terms, perhapsbecause of an interplay between local and party politics leading to more cross party
85 citations
TL;DR: The authors studied how a behavior (an idea, buying a product, having a disease, adopting a cultural fad or a technology) spreads among agents in an a social network that exhibits segregation or homophily (the tendency of agents to associate with others similar to themselves).
Abstract: We study how a behavior (an idea, buying a product, having a disease, adopting a cultural fad or a technology) spreads among agents in an a social network that exhibits segregation or homophily (the tendency of agents to associate with others similar to themselves). Individuals are distinguished by their types (e.g., race, gender, age, wealth, religion, profession, etc.) which, together with biased interaction patterns, induce heterogeneous rates of adoption. We identify the conditions under which a behavior diffuses and becomes persistent in the population. These conditions relate to the level of homophily in a society, the underlying proclivities of various types for adoption or infection, as well as how each type interacts with its own type. In particular, we show that homophily can facilitate diffusion from a small initial seed of adopters.
77 citations
TL;DR: This paper proposes a secure and effective user authentication and privacy preserving scheme with smart cards for wireless communications that works without password table, provides correct password change locally by the mobile user, non-repudiation, user friendliness, fairness in key agreement, and session keys establishment.
Abstract: In 2012, Li and Lee (C. T. Li and C. C. Lee, “A novel user authentication and privacy preserving scheme with smart cards for wireless communications,” Mathematical and Computer Modelling, vol. 55, nos. 1–2, pp. 35–44, 2012) proposed a novel user authentication and privacy preserving scheme with smart cards for wireless communications. However, in this paper, we show that Li-Lee’s scheme is vulnerable to three security weaknesses: (1) Li-Lee’s scheme fails to achieve strong authentication in login and authentication phases, (2) Li-Lee’s scheme fails to update the user’s password correctly in the password change phase, and (3) Li-Lee’s scheme fails strongly to protect replay attacks. In order to remedy those security flaws in Li-Lee’s scheme, we propose a secure and effective user authentication and privacy preserving scheme with smart cards for wireless communications. We show that our scheme is secure against various known types of attacks, such as user anonymity, perfect forward security, strong replay attack, impersonation and off-line password guessing attacks and parallel session attack, which makes our scheme more secure and practical for mobile wireless networking. Moreover, our scheme works without password table, provides correct password change locally by the mobile user, non-repudiation, user friendliness, fairness in key agreement, and session keys establishment between the mobile user and the foreign agent, between the mobile user and the home agent, and between the foreign agent and the home agent. Further, through the simulation results using the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool we show that our improved scheme is secure against passive and active attacks.
75 citations
TL;DR: In this paper, the authors compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the fixed rank nomination (FRN) scheme, and therefore accommodate the ranked and censored nature of the data.
Abstract: Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design.
40 citations
TL;DR: This paper proposes a theoretically motivated index of reciprocity appropriate for networks formed from repeated interactions based on probabilities of reciprocal or non-reciprocal relationships and examines the extent to which deviations from reciprocity in the observed network are partially traceable to the operation of these countervailing tendencies.
Abstract: A wide variety of networked systems in human societies are composed of repeated communications between actors. A dyadic relationship made up of repeated interactions may be reciprocal (both actors have the same probability of directing a communication attempt to the other) or non-reciprocal (one actor has a higher probability of initiating a communication attempt than other). In this paper we propose a theoretically motivated index of reciprocity appropriate for networks formed from repeated interactions based on these probabilities. We go on to examine the distribution of reciprocity in a large-scale social network built from trace-logs of over a billion cell-phone communication events across millions of actors in a large industrialized country. We find that while most relationships tend toward reciprocity, a substantial minority of relationships exhibit large levels of non-reciprocity. This is puzzling because behavioral theories in social science predict that persons will selectively terminate non-reciprocal relationships, keeping only those that approach reciprocity. We point to Research was sponsored by the Army Research Laboratory and was accomplished in part under Cooperative Agreement Number W911NF-09-2-0053, by the Defense Threat Reduction Agency (DTRA) grant HDTRA 1-09-1-0039 (Anthony Strathman and Zolt´ an Toroczkai), and by the National Science Foundation (NSF) grant BCS-0826958. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Special acknowledgments go to Albert L´´ Barab´ asi for providing the source data.
TL;DR: This paper proposes an active detection mechanism for NDP based attacks in IPv6 network to overcome these problems and experimental results illustrate the efficacy and performance of the scheme.
Abstract: Internet Protocol version 6 (IPv6) uses Network Discovery Protocol (NDP) to find the Media Access Control (MAC) address to communicate with hosts in a LAN. Like its predecessor, Address Resolution Protocol (ARP) in IPv4, NDP is stateless and lacks authentication by default. The traditional spoofing attacks for exploiting the IP to MAC resolution using ARP in IPv4 are also relevant in NDP. By using spoofed MAC addresses, a malicious host can also launch Denial-of-Service (DoS), Man-in-the-Middle(MiTM) attacks etc. in IPv6 network. Although there are various detection/prevention mechanisms available for IPv4, many of them are not yet implemented in IPv6 as the protocol is relatively new and slowly coming in use. Few mechanisms have been proposed for detection/prevention of these attacks in IPv6, but they either are non-scalable, computationally expensive, require management of cryptographic keys or change in the protocol itself. In this paper, we propose an active detection mechanism for NDP based attacks in IPv6 network to overcome these problems. Experimental results illustrate the efficacy and performance of the scheme.
TL;DR: This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient, which has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations.
Abstract: The distribution of degree in real world networks is generally highly skewed. Network researchers with different disciplinary backgrounds describe the distribution in different ways. Social scientists typically report variance, centralisation, or skewness. In contrast, mathematical physicists typically report power law exponents. In part, this difference reflects differences in the networks being examined, particularly network size, and consequent differences in distribution shape. In this presentation, I suggest that the Gini coefficient is an appropriate distribution shape measure for any network.
TL;DR: A measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed based on the influence effects have on decisions of individual actors in the network and demonstrates its utility on empirical data by analyzing an evolving friendship network of university freshmen.
Abstract: A measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed. The SAOM is a parametric model for statistical inference in longitudinal social networks. The complexity of the model makes the interpretation of inferred results difficult. So far, the focus is on significance tests while the relative importance of effects is usually ignored. Indeed, there is no established measure to determine the relative importance of an effect in a SAOM. We introduce such a measure based on the influence effects have on decisions of individual actors in the network. We demonstrate its utility on empirical data by analyzing an evolving friendship network of university freshmen. Assessing the relative importance of multiple explanatory variables in statistical models is a challenging task in all but the simplest cases. A general approach is not available such that measures of relative importance have to be individually defined for different models. But even for a specific model, a convincing definition of relative importance does not necessarily exist such that multiple ambiguous explications are conceivable and, in most cases, application-specific heuristics have to suffice. Especially, if variables are correlated, an explicit decomposition of importance is not possible. The existence of strong structural dependencies, however, is a characterizing feature of network data such that strong correlations between network effects are the rule and not the exception. This might be one reason why in statistical models for network data questions regarding the relative importance of effects are mostly ignored although, particularly in practical applications, information on the strength of an effect seems to be more useful and relevant than merely whether or not the effect exists. In this article, we propose a measure of relative importance of effects in the stochastic actor-oriented model (SAOM). The model was introduced by Snijders and is described, e.g., in Snijders (2005) and Snijders et al. (2010a). SAOMs are used to analyze social network panel data in order to identify network-specific social mechanisms, referred to as network effects, such as reciprocity, transitivity, or homophily, that may explain the unobserved evolution between observation moments.
TL;DR: Investigating the structure and composition of homeless men's social networks with a sample of men drawn randomly from meal lines on Skid Row in Los Angeles finds men who report chronic, long-term homelessness display greater social network fragmentation when compared to non-chronically homeless men.
Abstract: Homeless men are frequently unsheltered and isolated, disconnected from supportive organizations and individuals. However, little research has investigated these men's social networks. We investigate the structure and composition of homeless men's social networks, vis-a-vis short- and long-term homelessness with a sample of men drawn randomly from meal lines on Skid Row in Los Angeles. Men continuously homeless for the past six months display networks composed of riskier members when compared to men intermittently homeless during that time. Men who report chronic, long-term homelessness display greater social network fragmentation when compared to non-chronically homeless men. While intermittent homelessness affects network composition in ways that may be addressable with existing interventions, chronic homelessness fragments networks, which may be more difficult to address with those interventions. These findings have implications for access to social support from network members which, in turn, impacts the resources homeless men require from other sources such as the government or NGOs.
TL;DR: Outreach efforts to introduce school students to network science and why researchers who study networks should be involved in such outreach activities are discussed.
Abstract: We discuss our outreach efforts to introduce school students to network science and explain why researchers who study networks should be involved in such outreach activities. We provide overviews of modules that we have designed for these efforts, comment on our successes and failures, and illustrate the potentially enormous impact of such outreach efforts.
TL;DR: This paper considers alternative MAC protocols, compatible with IoT specificities, and proposes a strategy to enable heterogeneous MAC duty-cycle configurations among nodes in the network, reaching a compromise between energy consumption and reactivity.
Abstract: The Internet of Things (IoT) paradigm aims at connecting any object to the Internet (i.e. to the IP world). Due to the physical constraints (limited energy capacities) and deployment conditions (numerous autonomous devices scattered into an area) of such Things, power management and scalability are key issues in IoT deployments. While the problematics of the IP addressing have been successfully transposed to IoT networks, the dedicated IEEE 802.15.4 Medium Access Control standard lacks of scalability, provides insufficient energy-efficiency and thus fails to fulfill their needs. In this paper, we consider alternative MAC protocols, compatible with IoT specificities. These protocols realize energy gains by asynchronously alternating active and passive periods at the radio scale, thus allowing both energy-efficiency and scalability. For the time being, most real IoT deployments implement static and homogeneous duty-cycling (i.e. invariant and identical for each node in the network). Although preventing any node isolation, such method fails to address the dynamics of the network efficiently. We propose a strategy to enable heterogeneous MAC duty-cycle configurations among nodes in the network. We aim at granting each node a specific sleep-depth, according to criteria specific to the deployment (e.g. applicative criteria, location in the routing structure). To implement this idea, the nodes are divided into disjoint subsets, each of them standing for a given duty-cycle configuration and leading to a network performance managed at its best (e.g. energy consumption, loss-rate, delays). We detail to what extent our approach preserves network connectivity with coherent heterogeneous duty-cycling, thus reaching a compromise between energy consumption and reactivity. The presented experimental campaign was led over the IoT SensLAB testbed. It demonstrates that our solutions provide up to 61% energy saving, preserve the loss-rate below 10% and guarantee the connectivity of the network. They thus offer a better compromise between energy-efficiency and network performances than any homogeneous MAC configuration.
TL;DR: A new network analytic approach is introduced that can disentangle the effects of crowd identification and sports participation on individual behavior and shows that both shared identities and joint participation were associated with all stages of drinking, controlling for friends' influence.
Abstract: Self-identification with peer crowds (jocks, popular kids, druggies, etc.) has an important influence on adolescent substance use behavior. However, little is known about the impact of the shared nature of crowd identification on different stages of adolescent drinking behavior, or the way crowd identification interacts with participation in school-sponsored sports activities. This study examines drinking influences from (1) peers with shared crowd identities, and (2) peers who jointly participate in organized sports at their school (activity members). This study introduces a new network analytic approach that can disentangle the effects of crowd identification and sports participation on individual behavior. Using survey data from adolescents in five high schools in a predominantly Hispanic/Latino district (N = 1,707), this paper examines the association between social influences and each stage of drinking behavior (intention to drink, lifetime, past-month, and binge drinking) by conducting an ordinal regression analysis. The results show that both shared identities and joint participation were associated with all stages of drinking, controlling for friends' influence. Additionally, shared identification overlapped with joint participation was associated with more frequent drinking. Related policy implications are discussed.
TL;DR: A variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence outperformed a relocation heuristic and a tabu search method for the same problem.
Abstract: This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining to the votes of member countries on resolutions in the United Nations General Assembly. A comparative analysis revealed that the VNS heuristic often provided slightly better criterion function values than RH and TS, and that these small differences in criterion function values could sometimes be associated with substantial differences in the actual partitions obtained. Overall, the results suggest that the VNS heuristic is a promising approach for blockmodeling of two-mode binary networks. Recommendations for extensions to stochastic blockmodeling applications are provided.
TL;DR: The findings show that human subjects can use the elicitation tool effectively, supplying attribute and edge information to update a network indicative of a covert one.
Abstract: The study of covert networks is plagued by the fact that individuals conceal their attributes and associations. To address this problem, we develop a technology for eliciting this information from qualitative subject-matter experts to inform statistical social network analysis. We show how the information from the subjective probability distributions can be used as input to Bayesian hierarchical models for network data. In the spirit of “proof of concept,” the results of a test of the technology are reported. Our findings show that human subjects can use the elicitation tool effectively, supplying attribute and edge information to update a network indicative of a covert one.
TL;DR: The computational challenges and model selection decisions involved in network motif profiling are considered and three case studies concerning the analysis of Wikipedia edit networks, YouTube spam campaigns, and peer-to-peer lending in the Prosper marketplace are presented.
Abstract: We assess the potential of network motif profiles to characterize ego-networks in much the same way that a bag-of-words strategy allows text documents to be compared in a vector space framework. This is potentially valuable as a generic strategy for comparing nodes in a network in terms of the network structure in which they are embedded. In this paper, we consider the computational challenges and model selection decisions involved in network motif profiling. We also present three case studies concerning the analysis of Wikipedia edit networks, YouTube spam campaigns, and peer-to-peer lending in the Prosper marketplace.
TL;DR: It is found that under certain conditions, the size of information epidemic could even decrease with the growing size of the online social network, in stark contrast to that in a single network.
Abstract: We study the diffusion behavior of real-time information in an overlaying social-physical network. Typically, real-time information is valuable only for a limited time duration and needs to be delivered before its deadline, indicating that real-time information is more likely to spread among friends within a “social proximity.” With this insight, we consider a physical information network which consists of many cliques and assume that real-time information can spread quickly within a clique. Conjoint to this physical information network, there are online social networks where the information can propagate via websites such as Facebook, Twitter, Youtube, etc. Capitalizing on the theory of inhomogeneous random graph, we analytically characterize the size of information epidemic. One interesting finding is that a larger size online social network, with the same degree distribution, may not necessarily yield a larger size of information epidemic in this overlaying social-physical network. In fact, under certain conditions, the size of information epidemic could even decrease with the growing size of the online social network. This is in stark contrast to that in a single network.
TL;DR: An innovative attack script generation and injection approach to evaluate the security of the communication system and consequently detect its security flaws and demonstrates the effectiveness of the attack injection approach in the role of detecting security vulnerabilities in the Communication system.
Abstract: Next Generation Network (NGN) is a completely new architectural concept for providing end-users with voice, video, and all sorts of data services. However, secure communication is a key and challenging requirement for NGN service providers. In this paper, we propose an innovative attack script generation and injection approach to evaluate the security of the communication system and consequently detect its security flaws. We apply attack modeling technique to describe the system vulnerabilities and generate system context attack scenarios. The attack scenarios are then refined to executable attack scripts which are executed by communication testing tools in charge of emulating the system attacks. The approach is applied to Wireless Application Protocol (WAP), which is a customized protocol used in resource constrained mobile devices. We performed experiments using specific attacks of the mobile protocol such as Denial of Service (DoS) and Message Truncation attacks. The experiments results demonstrate the effectiveness of the attack injection approach in the role of detecting security vulnerabilities in the communication system.
TL;DR: Adaptive Probabilistic Marking scheme (APM) is focused on the probability with which a router marks a packet, and APM can cooperate with other probabilistic marking schemes.
Abstract: IP traceback can be used to find direct generator(s) and path(s) of attacking traffic. Probabilistic marking schemes, as one type of IP traceback technologies, have been most studied, but they are difficult to fast reconstruct attacking path(s) and defend against spoofed marks generated by attacking source(s). In this paper, we present Adaptive Probabilistic Marking scheme (APM). In APM, when each packet enters the first-hop router, its TTL value is set to a uniform value, and when it is forwarded by routers in the network, each intermediate router decreases the TTL value by one. Consequently, each intermediate router may infer the router-level hop number that each packet has already traveled, and then correspondingly marks the packet with the probability inversely proportional to the router-level hop number. APM is focused on the probability with which a router marks a packet, and APM can cooperate with other probabilistic marking schemes. NS2 simulation experiments prove that, in APM, the time for the victim to receive necessary marks for the path reconstruction is reduced by more than 20% compared with existing probabilistic marking schemes, and spoofed marks cannot reach the victim and influence the traceback process.
TL;DR: This paper studies how topological and geometric properties of embedded graphs influence the hop stretch and design embedding heuristics that yield minimal hop stretch greedy embeddings and verify their effectiveness on models of synthetic graphs.
Abstract: Greedy embedding is a graph embedding that makes the simple greedy geometric packet forwarding successful for every source-destination pair. It is desirable that graph embeddings also yield low hop overhead (stretch) of the greedy paths over the corresponding shortest paths. In this paper we study how topological and geometric properties of embedded graphs influence the hop stretch. Based on the obtained insights, we design embedding heuristics that yield minimal hop stretch greedy embeddings and verify their effectiveness on models of synthetic graphs. Finally, we verify the effectiveness of our heuristics on instances of several classes of large, real-world network graphs.
TL;DR: This paper proposes an innovative technique to geolocate the AS connections retrieved from BGP raw data, in order to highlight the Internet characteristics both at a continental and national level.
Abstract: Research to date has analyzed the Internet AS-level topology at a worldwide level of detail. Every inference found for an AS is extrapolated from the global set of AS paths gathered from public monitors, independently of the geographic location of the ASes. This approach is useful when the Internet is analyzed at a very coarse level. However, it may be misleading if the analysis is more focused on a specific geographical region. The risk is that the particular characteristics that the Internet has in that region may be lost. An AS connection that has been identified in a global analysis may hide multiple connections located in different geographic regions, each with its own characteristics. Moreover, a couple of ASes may establish different economic relationships in each geographic region where they are connected. In this paper we propose an innovative technique to geolocate the AS connections retrieved from BGP raw data, in order to highlight the Internet characteristics both at a continental and national level. The analyses that we performed revealed some regional characteristics, in terms of graph properties and inter-AS economic relationships, which should be taken into account in a future analysis of the Internet.
TL;DR: An analysis of the relationship between unexplained racial/ethnic wage differentials and social network segregation, as measured by inbreeding homophily, in the US and Estonia finds a strong relationship between the size of the wage differential and network segregation.
Abstract: This paper analyzes the relationship between unexplained racial/ethnic wage differentials on the one hand and social network segregation, as measured by inbreeding homophily, on the other. Our analysis is based on both the US and Estonian surveys, supplemented with the Estonian telephone communication data. In the case of Estonia we consider the regional variation in economic performance of the Russian minority, and in the US case we consider the regional variation in black-white differentials. Our analysis finds a strong relationship between the size of the wage differential and network segregation: Regions with more segregated social networks exhibit larger unexplained wage gaps.
TL;DR: This work investigated rich club organization in the human brain in datasets that recorded weighted projections among different anatomical regions of the cerebral cortex, recorded from several cohorts of healthy human volunteers.
Abstract: Does the human brain have a central connective core, and, if so, how costly is it? Noninvasive imaging data allow the construction of network maps of the human brain, recording its structural and functional connectivity. A number of studies have reported on various characteristic network attributes, such as a tendency toward local clustering, high global efficiency, the prevalence of specific network motifs, and a pronounced community structure with several anatomically and functionally defined modules and interconnecting hub regions (Bullmore & Sporns, 2009; van den Heuvel & Hulshoff Pol, 2010; Sporns, 2011). Hubs are of particular interest in studies of the brain since they may play crucial roles in integrative processes and global brain communication, thought to be essential for many aspects of higher brain function. Indeed, hubs have been shown to correspond to brain regions that exhibit complex physiological responses and maintain widespread and diverse connection profiles with other parts of the brain. We asked if, in addition to being highly connected, brain hubs would also exhibit a strong tendency to be mutually interconnected, forming what has been called a “rich club” (Colizza et al., 2006). Rich club organization is present in a network if sets of high-degree nodes exhibit denser mutual connections than predicted on the basis of the degree sequence alone. We investigated rich club organization in the human brain in datasets that recorded weighted projections among different anatomical regions of the cerebral cortex, recorded from several cohorts of healthy human volunteers (van den Heuvel & Sporns, 2011; van den Heuvel et al., 2012).
TL;DR: This paper presents a mobility model for shopping mall environments based on real traces that captures heterogeneous behaviour of nodes and several different mobility characteristics at a lower level of abstraction and produces contact traces distributions which approximate those of the collected traces.
Abstract: Simulations of mobile ad-hoc, sensor, and mesh networks strongly rely on mobility models because they have a major influence on the performance of protocols. Results obtained with an unrealistic model may not reflect the true performance of protocols, applications and algorithms in real environments. In this paper, we present a mobility model for shopping mall environments based on real traces. Such environments offer all of the elements required to build large-scale people-centric ad-hoc networks. In many cases, shopping malls are tens of thousands of square metres in area and crowded much of the time. We ran a field trial to collect Bluetooth contact data from shop employees and clerks in a shopping mall over six days. We analysed the collected contact traces to guide the design of our Shopping Mall mobility model. Unlike the majority of existing synthetic mobility models our Shopping Mall mobility model captures heterogeneous behaviour of nodes and several different mobility characteristics at a lower level of abstraction. We show that our synthetic mobility model produces contact traces distributions which approximate those of the collected traces.
TL;DR: A Mobile Traversal Algorithm (MTA) is proposed, for mobile sensor nodes to cover a rectangular region of interest (ROI) and makes MSNs to travel shorter distances to extend effective operational duration of the network and also to provide fault tolerance mechanism.
Abstract: Mobile sensor nodes are featured with node mobility along with standard sensor functions. They can move around after being deployed. In many situations placement of static sensor nodes might not be possible and human intervention is not feasible. Mobile sensor nodes are very useful in such hazardous and disastrous situations. When a group of mobile sensor nodes are deployed to cover an area for search operations, coordination among the deployed nodes is very important. A mobile sensor node (MSN) depletes more energy during the traversal. Whenever there arises a failure, in one of the deployed mobile sensor nodes, the remaining fault-free mobile sensor nodes should travel to cover the remaining uncovered area. In this work we propose a Mobile Traversal Algorithm (MTA), for mobile sensor nodes to cover a rectangular region of interest (ROI). It makes MSNs to travel shorter distances to extend effective operational duration of the network and also to provide fault tolerance mechanism.