On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Analysis of the autonomous system network topology
ACM SIGCOMM Computer Communication Review
A social hypertext model for finding community in blogs
Proceedings of the seventeenth conference on Hypertext and hypermedia
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Observing the evolution of internet as topology
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
IEEE Transactions on Knowledge and Data Engineering
Ten years in the evolution of the internet ecosystem
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Personalized emerging topic detection based on a term aging model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Community detection has been one of the major topics in complex network research. Recently, several greedy algorithms for networks of millions of nodes have been proposed, but one of their limitations is inconsistency of outcomes [1]. Kwak et al. propose an iterative reinforcing approach to eliminate inconsistency in detected communities. In this paper we delve into structural characteristics of communities identified by Kwak's method with 12 real networks. We find that about 40%of nodes are grouped into communities in an inconsistent way in Orkut and Cyworld. Interestingly, they are only two out of 12 networks whose community size distribution follow power-law. As a first step towards interpretation of communities, we use Guimera and Amaral's method [2]to classify nodes into seven classes based on the z-score and the participation coefficient. Using the z-P analysis, we identify the roles of nodes in Karate and Autonomous System (AS) networks and match them against known roles for evaluation. We apply topological mesoscale information to compare two AS produced by Oliveira et al. [3], and Dhamdhere and Dovrolis [4] We report that even though their AS graphs differ in size, their topological characteristics are very similar.