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The structure of information pathways in a social communication network
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International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Las Vegas, Nevada, USA
SESSION: Research papers table of contents
Pages 435-443  
Year of Publication: 2008
ISBN:978-1-60558-193-4
Authors
Gueorgi Kossinets  Cornell University, Ithaca, NY, USA
Jon Kleinberg  Cornell University, Ithaca, NY, USA
Duncan Watts  Yahoo!, New York, NY, USA
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of "distance" in the underlying social network by measuring the minimum time required for information to spread from one node to another - a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges - a finding that sheds new light on the relationship between tie strength and connectivity in social networks.


REFERENCES

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Collaborative Colleagues:
Gueorgi Kossinets: colleagues
Jon Kleinberg: colleagues
Duncan Watts: colleagues