Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
On the bias of traceroute sampling: or, power-law degree distributions in regular graphs
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Exploring networks with traceroute-like probes: theory and simulations
Theoretical Computer Science - Complex networks
Random walks in peer-to-peer networks: algorithms and evaluation
Performance Evaluation - P2P computing systems
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On unbiased sampling for unstructured peer-to-peer networks
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Uniform Data Sampling from a Peer-to-Peer Network
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Sampling large Internet topologies for simulation purposes
Computer Networks: The International Journal of Computer and Telecommunications Networking
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of online social relations in volume vs interaction: a case study of cyworld
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Metropolis Algorithms for Representative Subgraph Sampling
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Evolution analysis of a mobile social network
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Benefits of bias: towards better characterization of network sampling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Role-dynamics: fast mining of large dynamic networks
Proceedings of the 21st international conference companion on World Wide Web
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Sparsification and sampling of networks for collective classification
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
FakeBook: Detecting Fake Profiles in On-Line Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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While most research in online social networks (OSNs) in the past has focused on static friendship networks, social network activity graphs are quite important as well. However, characterizing social network activity graphs is computationally intensive; reducing the size of these graphs using sampling algorithms is critical. There are two important requirements---the sampling algorithm must be able to preserve core graph characteristics and be amenable to a streaming implementation since activity graphs are naturally evolving in a streaming fashion. Existing approaches satisfy either one or the other requirement, but not both. In this paper, we propose a novel sampling algorithm called Streaming Time Node Sampling (STNS) that exploits temporal clustering often found in real social networks. Using real communication data collected from Facebook and Twitter, we show that STNS significantly out-performs state-of-the-art sampling mechanisms such as node sampling and Forest Fire sampling, across both averages and distributions of several graph properties.