On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Topology modeling via cluster graphs
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Towards capturing representative AS-level Internet topologies
Computer Networks: The International Journal of Computer and Telecommunications Networking
A first-principles approach to understanding the internet's router-level topology
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Communications of the ACM - The Blogosphere
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Understanding internet topology: principles, models, and validation
IEEE/ACM Transactions on Networking (TON)
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Observing the evolution of internet as topology
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
A framework for fast community extraction of large-scale networks
Proceedings of the 17th international conference on World Wide Web
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Community Learning by Graph Approximation
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Understanding topological mesoscale features in community mining
COMSNETS'10 Proceedings of the 2nd international conference on COMmunication systems and NETworks
The little engine(s) that could: scaling online social networks
Proceedings of the ACM SIGCOMM 2010 conference
Strange bedfellows: community identification in bittorrent
IPTPS'10 Proceedings of the 9th international conference on Peer-to-peer systems
Listen to me if you can: tracking user experience of mobile network on social media
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Profiling-By-Association: a resilient traffic profiling solution for the internet backbone
Proceedings of the 6th International COnference
Topic-based social network analysis for virtual communities of interests in the Dark Web
ACM SIGKDD Workshop on Intelligence and Security Informatics
Multiple level views on the adherent cohesive subgraphs in massive temporal call graphs
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Topic-based social network analysis for virtual communities of interests in the dark web
ACM SIGKDD Explorations Newsletter
Leveraging Social Network Analysis with Topic Models and the Semantic Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Sharing graphs using differentially private graph models
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
The little engine(s) that could: scaling online social networks
IEEE/ACM Transactions on Networking (TON)
Multi-scale dynamics in a massive online social network
Proceedings of the 2012 ACM conference on Internet measurement conference
The contagion of malicious behaviors in online games
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Analyzing future communities in growing citation networks
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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
Leveraging social network analysis with topic models and the Semantic Web extended
Web Intelligence and Agent Systems - Web Intelligence and Communities
Hi-index | 0.00 |
Online social networks pose significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. Many algorithms based on various metrics have been proposed for communities in networks [18, 24], but a few algorithms scale to very large networks. Three recent community identification algorithms, namely CNM [16], Wakita [59], and Louvain [10], stand out for their scalability to a few millions of nodes. All of them use modularity as the metric of optimization. However, all three algorithms produce inconsistent communities every time the ordering of nodes to the algorithms changes. We propose two quantitative metrics to represent the level of consistency across multiple runs of an algorithm: pairwise membership probability and consistency. Based on these two metrics, we propose a solution that improves the consistency without compromising the modularity. We demonstrate that our solution to use pairwise membership probabilities as link weights generates consistent communities within six or fewer cycles for most networks. However, our iterative, pairwise membership reinforcing approach does not deliver convergence for Flickr, Orkut, and Cyworld networks as well for the rest of the networks. Our approach is empirically driven and is yet to be shown to produce consistent output analytically. We leave further investigation into the topological structure and its impact on the consistency as future work. In order to evaluate the quality of clustering, we have looked at 3 of the 48 communities identified in the AS graph. Surprisingly, all have either hierarchical, geographical, or topological interpretations to their groupings. Our preliminary evaluation of the quality of communities is promising. We plan to conduct more thorough evaluation of the communities and study network structures and their evolutions using our approach.