9. The probability that a person visits a location. ~ means that two locations are equivalent.  Grids -> close to the border of two different cells.  Threshold -> 31m and 30m! To solve above problems, a density function could be used: C d is a parameter that determines the impact of the distance. d<1km walking distance 5k>d driving distance Background ModelingRelationship Mining

10. Popularity of a location. The set of location records of user i visiting location loc k : The ratio that user i’s visit o the entire population: Shannon entropy of a location can be estimated using the probability vector of all users visiting this location: Lower entropy means that this place is visited by few users. Background ModelingRelationship Mining

11. We aim to determine the significance of a meeting event e k between users i and j at location loc k. Personal factor weight (more weights for the places that users rarely visit): Strength of a meeting (average weight X numbers): 60% of friends and 5% on non-friends have weights higher than 18. **CDF: cumulative distribution function Background ModelingRelationship Mining

12. The problem of location factor is that it should be calculated from location history of all users. Higher entropy -> lower weight Combining global and local factors: Background ModelingRelationship Mining

13. If two events are temporally closer -> lower weight for the meeting! Background ModelingRelationship Mining

14. Background ModelingRelationship Mining