Putting Contacts into Context: Mobility Modeling beyond Inter-Contact Times
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MobiHoc 2011
![Mobility Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[email_address] RPGM SIMPS SLAW TVCM CMM HCMM SWIM GHOST](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Putting Contacts into Context: Mobility Modeling beyond Inter-Contact Times
- 1. Putting Contacts into Context Mobility Modeling beyond Inter-Contact Times Theus Hossmann ETH Zürich, Switzerland Thrasyvoulos Spyropoulos EURECOM, France Franck Legendre ETH Zürich, Switzerland
- 3. Known Mobility Properties MASTERED MASTERED [email_address] Individual Properties Diurnal & weekly periodicity [Henderson et al MobiCom `04] Location preference [Tuduce et al Infocom `05] Power law trip length [Lee et al Infocom `09] Pairwise Properties Heavy tailed aggregate inter-contact times (exponential cut -off) [Chaintreau et al Infocom `06] [Karagiannis et al MobiCom `07] [Cai et al MobiCom `07] Individual pairs with various distributions [Leguay et al Autonomics `07)]
- 5. Methodology [email_address] Mobility Model ?? Synthetic Trace Contact Graph Contact Trace Contact Graph Community Structure? Modularity Community Connections? Bridges Structural Properties?
- 6. Mobility Traces [email_address] Self-reported “check-ins” (like Foursquare) ~ 440’000 users (October 2010) ~ 16.7 Mio check-ins to ~ 1.6 Mio spots 473 “power users” who check-in at least 5 out of 7 days
- 7. Mobility Models [email_address] TVCM (location based) [Spyropoulos et al ToN `09] HCMM (social network based) [Boldrini et al Comp. Comm. `10] SLAW (location based) [Lee et al Infocom `09]
- 9. The Contact Graph [email_address]