The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Planetary-scale views on a large instant-messaging network
Abstract
We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.
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Cites
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Holme, P., & Saramäki, J. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.001 · Cited by 2,736
“Importantly, there is an increasing body of evidence of very large heterogeneity in the timings of such interactions, from burstiness in patterns of communication via electronic and physical mail [10,11,17,18,114,115], mobile telephone calls and text messages [20,25–27], instant messaging [23], and patterns in proximi…”
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Metadata
- Authors
- Leskovec, J., & Horvitz, E.
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 915-924
- OpenAlex
- W2157579446
- Cited by
- 616
- References
- 20
- FWCI
- 61.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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