Find Me If You Can: Improving Geographical Prediction

with Social and Spatial Proximity

Part 1: Introduction

Importance of geolocation

Finding compromised accounts (prevent security breaches).

Personalization of information based on location.

MotivationIP address typically provide accuracy at

the city level.results are inconsistent.Geo-IP databases require constant

maintenance.Other geolocation strategies are non

transparent.

Part 3: use of observations in a predictive model

Part 2: relationships

Part 1: Introduction

Paper outlinestudy 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.

Factors that affect relationshipsSocial Norms.Distance.Communication technologies.

• 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

No bias problem.

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 .

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.

• we can get a good fit to a curve of the form . The exponent very close to c = −1.

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.

ranku(v) := |{w : d(u,w) < d(u, v)}|.we do see a nice smooth curve, again with an

exponent of close to −1.

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).

Part 3: use of observations in a predictive model

Part 2: relationships

Part 1: Introduction

)

. = 0.0019 (taken from slide 12).

attempt to recover addresses of 75 % of individuals.iteratively using the newly guessed locations as

input as well as the locations provided by users.

Prediction performance as a function of friend count.

A good trade-off is 5+blend.

BenefitsInfo about relationships with greater

accuracy and in greater depth.The new algorithm.

Part 3: use of observations in a predictive model

Part 2: relationships

Part 1: Introduction

Future workFuture work can improve even more the

accuracy.Using social gathering.

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

ones.