Crawling Facebook for social network analysis purposes
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Multi agent system for historical information retrieval from online social networks
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Online social honeynets: trapping web crawlers in OSN
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Privacy-aware and scalable content dissemination in distributed social networks
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Community detection in Social Media
Data Mining and Knowledge Discovery
Putting humans in the loop: Social computing for Water Resources Management
Environmental Modelling & Software
Allowing continuous evaluation of citizen opinions through social networks
EGOVIS'12/EDEM'12 Proceedings of the 2012 Joint international conference on Electronic Government and the Information Systems Perspective and Electronic Democracy, and Proceedings of the 2012 Joint international conference on Advancing Democracy, Government and Governance
Discovering links among social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Bridge analysis in a Social Internetworking Scenario
Information Sciences: an International Journal
Multi agent system approach for vulnerability analysis of online social network profiles over time
International Journal of Knowledge and Web Intelligence
Crawling Social Internetworking Systems
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Does social contact matter?: modelling the hidden web of trust underlying twitter
Proceedings of the 22nd international conference on World Wide Web companion
Moving from social networks to social internetworking scenarios: The crawling perspective
Information Sciences: an International Journal
| Hi-index | 0.00 |
Extensive research has been conducted on top of online social networks (OSNs), while little attention has been paid to the data collection process. Due to the large scale of OSNs and their privacy control policies, a partial data set is often used for analysis. The data set analyzed is decided by many factors including the choice of seeds, node selection algorithms, and the sample size. These factors may introduce biases and further contaminate or even skew the results. To evaluate the impact of different factors, this paper examines the OSN graph crawling problem, where the nodes are OSN users and the edges are the links (or relationship) among these users. More specifically, by looking at various factors in the crawling process, the following problems are addressed in this paper:* Efficiency: How fast different crawlers discover nodes/links;* Sensitivity: How different OSNs and the number of protected users affect crawlers;* Bias: How major graph properties are skewed.To the best of our knowledge, our simulations on four real world online social graphs provide the first in-depth empirical answers to these questions.