2. Part 1: Introduction

3. Importance of geolocation Finding compromised accounts (prevent security breaches). Personalization of information based on location.

4. Motivation IP address typically provide accuracy at the city level. results are inconsistent. Geo-IP databases require constant maintenance. Other geolocation strategies are non transparent.

5. Part 3: use of observations in a predictive model Part 2: relationships Part 1: Introduction

6. Paper outline study the relationship between geography and friendship. they use the Facebook social network in order to study the relationship. Some users provide their addresses. we get 30.6 million edges between individuals with known location.

7. Factors that affect relationships Social Norms. Distance. Communication technologies.

8. Males are significantly more likely to share their address information than females. users that share their addresses tend to have many more friends. Supplying addresses on Facebook

9. No bias problem.

10. low density: power-law with exponent -1.37. high density: power-law with exponent -3.07. 96% of people live in areas before the transition point on exponent -1.37.

11. we see that the curves increase linearly only for a small distance. we increase the radius and expect to find an increase in the population. on the other hand, we move further away from urban centers to rural areas.

13. for short distances the probability is higher in lower density areas at about 50 miles the three curves converge. at long distances, people in high density areas being more likely to be friends.

14. ranku(v) := |{w : d(u,w) < d(u, v)}|. we do see a nice smooth curve, again with an exponent of close to −1.

15. All the curves with exponent about −1. higher at low ranks for people in less dense areas, and higher at high ranks for people in more dense areas (cosmopolitan effect).

16. Part 3: use of observations in a predictive model Part 2: relationships Part 1: Introduction

19. attempt to recover addresses of 75 % of individuals. iteratively using the newly guessed locations as input as well as the locations provided by users.

20. Prediction performance as a function of friend count.

21. A good trade-off is 5+blend.

22. Benefits Info about relationships with greater accuracy and in greater depth. The new algorithm.

23. Part 3: use of observations in a predictive model Part 2: relationships Part 1: Introduction

24. Future work Future work can improve even more the accuracy. Using social gathering.

25. attaching time stamps to data. More weight to new friendships than old ones.