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Parallel algorithms for evaluating centrality indices in realworld networks
 In Proceedings of the International Conference on Parallel Processing (ICPP
, 2006
"... This paper discusses fast parallel algorithms for evaluating several centrality indices frequently used in complex network analysis. These algorithms have been optimized to exploit properties typically observed in realworld large scale networks, such as the low average distance, high local density, ..."
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Cited by 54 (11 self)
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This paper discusses fast parallel algorithms for evaluating several centrality indices frequently used in complex network analysis. These algorithms have been optimized to exploit properties typically observed in realworld large scale networks, such as the low average distance, high local density, and heavytailed power law degree distributions. We test our implementations on real datasets such as the web graph, proteininteraction networks, movieactor and citation networks, and report impressive parallel performance for evaluation of the computationally intensive centrality metrics (betweenness and closeness centrality) on highend shared memory symmetric multiprocessor and multithreaded architectures. To our knowledge, these are the first parallel implementations of these widelyused social network analysis metrics. We demonstrate that it is possible to rigorously analyze networks three orders of magnitude larger than instances that can be handled by existing network analysis (SNA) software packages. For instance, we compute the exact betweenness centrality value for each vertex in a large US patent citation network (3 million patents, 16 million citations) in 42 minutes on 16 processors, utilizing 20GB RAM of the IBM p5 570. Current SNA packages on the other hand cannot handle graphs with more than hundred thousand edges. 1
Relevance of Massively Distributed Explorations of the Internet Topology: Simulation Results
, 2005
"... Internet maps are generally constructed using the traceroute tool from a few sources to many destinations. It appeared recently that this exploration process gives a partial and biased view of the real topology, which leads to the idea of increasing the number of sources to improve the quality of ..."
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Cited by 42 (14 self)
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Internet maps are generally constructed using the traceroute tool from a few sources to many destinations. It appeared recently that this exploration process gives a partial and biased view of the real topology, which leads to the idea of increasing the number of sources to improve the quality of the maps. In this paper, we present a set of experiments we have conduced to evaluate the relevance of this approach. It appears that the statistical properties of the underlying network have a strong influence on the quality of the obtained maps, which can be improved using massively distributed explorations. Conversely, we show that the exploration process induces some properties on the maps. We validate our analysis using realworld data and experiments and we discuss its implications.
NetworkAware Behavior Clustering of Internet End Hosts
 in Proceedings of IEEE INFOCOM
, 2011
"... Abstract—This paper explores the behavior similarity of Internet end hosts in the same network prefixes. We use bipartite graphs to model network traffic, and then construct onemode projection graphs for capturing socialbehavior similarity of end hosts. By applying a simple and efficient spectral ..."
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Cited by 5 (3 self)
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Abstract—This paper explores the behavior similarity of Internet end hosts in the same network prefixes. We use bipartite graphs to model network traffic, and then construct onemode projection graphs for capturing socialbehavior similarity of end hosts. By applying a simple and efficient spectral clustering algorithm, we perform networkaware clustering of end hosts in the same prefixes into different behavior clusters. Based on informationtheoretical measures, we find that the clusters exhibit distinct traffic characteristics which provides improved interpretations of the separated traffic compared with the aggregated traffic of the prefixes. Finally, we demonstrate the applications of exploring behavior similarity in profiling network behaviors and detecting anomalous behaviors through synthetic traffic that combines Internet backbone traffic and packet traces from real scenarios of worm propagations and denial of service attacks. I.
ªKnowledge Discovery
 in Large Spatial Databases,º Proc. Far East Workshop Geographic Information Systems
, 1993
"... method for placing traceroutelike topology ..."
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Complex Network Metrology
"... In order to study some complex networks like the Internet, the Web, social networks or biological networks, one first has to explore them. This gives a partial and biased view of the real object, which is generally assumed to be representative of the whole. However, up to now nobody knows how and ho ..."
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Cited by 4 (1 self)
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In order to study some complex networks like the Internet, the Web, social networks or biological networks, one first has to explore them. This gives a partial and biased view of the real object, which is generally assumed to be representative of the whole. However, up to now nobody knows how and how much the measure influences the results. Using the example of the Internet and a rough model of its exploration process, we show that the way a given complex network is explored may strongly influence the observed properties. This leads us to argue for the necessity of developing a science of metrology of complex networks. Its aim would be to study how the partial and biased view of a network relates to the properties of the whole network. Introduction. Some complex networks of high interest can only be known after an exploration process. This is in particular true for the Internet (interconnection of computers), the Web (links between pages), social networks (acquaintance relations for example), and biological networks
Impact of Random Failures and Attacks on Poisson and PowerLaw Random Networks
, 2009
"... It appeared recently that the underlying degree distribution of networks may play a crucial role concerning their robustness. Empiric and analytic results have been obtained, based on asymptotic and meanfield approximations. Previous work insisted on the fact that powerlaw degree distributions ind ..."
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Cited by 4 (1 self)
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It appeared recently that the underlying degree distribution of networks may play a crucial role concerning their robustness. Empiric and analytic results have been obtained, based on asymptotic and meanfield approximations. Previous work insisted on the fact that powerlaw degree distributions induce high resilience to random failure but high sensitivity to attack strategies, while Poisson degree distributions are quite sensitive in both cases. Then, much work has been done to extend these results. We aim here at studying in depth these results, their origin, and limitations. We review in detail previous contributions and give full proofs in a unified framework, and identify the approximations on which these results rely. We then present new results aimed at enlightening some important aspects. We also provide extensive rigorous experiments which help evaluate the relevance of the analytic results. We reach the conclusion that, even if the basic results of the field are clearly true and important, they are in practice much less striking than generally thought. The differences between random failures and attacks are not so huge and can be explained with simple facts. Likewise, the differences in the behaviors induced by powerlaw and Poisson distributions are not as striking as often claimed.
Basic Notions for the Analysis of Large Affiliation Networks / Bipartite Graphs
, 2008
"... Many realworld complex networks actually have a bipartite nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many adhoc tools have been designed for the study of special cases, very few exist to an ..."
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Cited by 3 (0 self)
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Many realworld complex networks actually have a bipartite nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many adhoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse classical complex networks to the bipartite case. To achieve this, we introduce a set of simple statistics, which we discuss by comparing their values on a representative set of realworld networks and on their random versions.
Evolving Clustered Random Networks
, 2008
"... We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus ..."
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Cited by 2 (0 self)
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We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus
Discovering shared interests in online social networks
 in Proceedings of IEEE ICDCS Workshop on PeertoPeer Computing and Online Social Networking (HOTPOST
, 2012
"... Abstract—The capacity of rapidly disseminating information such as latest news headlines has made online social networks a popular and disruptive venue for spreading influence and distributing contents. Given the importance of online social networks, it becomes increasingly imperative to understand ..."
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Abstract—The capacity of rapidly disseminating information such as latest news headlines has made online social networks a popular and disruptive venue for spreading influence and distributing contents. Given the importance of online social networks, it becomes increasingly imperative to understand the shared interests of users on the popular information or contents that circulate through these networks. This paper proposes a novel graphical approach based on bipartite graphs and onemode projection graphs to model the interactions of users and information and to capture the shared interests of users on the information. The experiments based on datasets collected from Digg, a popular social news aggregation site, have demonstrated the proposed approach is able to discover inherent clusters of users and information within online social networks. The evaluation results also show that these clusters exhibit distinct characteristics. To the best of our knowledge, this paper is the first attempt to apply bipartite graphs and onemode projections to shed light on the interactions of people and information in online social networks and to discover the clustered nature of users and contents. I.