Connectivity of random graphs and mobile networks: validation of Monte Carlo simulation results
Proceedings of the 2001 ACM symposium on Applied computing
Random Evolution in Massive Graphs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
When social networks cross boundaries: a case study of workplace use of facebook and linkedin
Proceedings of the ACM 2009 international conference on Supporting group work
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Groups without tears: mining social topologies from email
Proceedings of the 16th international conference on Intelligent user interfaces
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Simplifying friendlist management
Proceedings of the 21st international conference companion on World Wide Web
Regroup: interactive machine learning for on-demand group creation in social networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In a social network, users can sort members of their social graph into friend lists to both understand the social structures within the graph and control the flow of incoming and outgoing information. To reduce the user-effort required to create these lists, previous work has developed techniques for generating friend-lists in a static social graph. This paper considers the user effort required to create friend lists in an evolving graph. We have developed several new initial quantitative metrics to capture this effort, and identified an initial technique for modeling graph growth. We have used these metrics and model to compare two techniques for evolving friend lists when the social graph grows: manual evolution - the user evolves friend lists using no external tools -- and full recommendation -- an existing state of the art tool recommends a whole new set of friend lists. In these comparisons, we used the friend lists of 12 individuals, and simulated the growth of their social graphs and friend lists using our graph-growth model. Intuitively, when the graph evolves by a small (large) amount, the manual (automatic) approach should perform better. Our experiments show that full recommendation performs better than manual when the social graph changes by more than 1%, and yields an almost complete reduction in effort in the best cases.