Suggesting Friends Using the Implicit Social Graph

Title

Yusuf Simonson

The Social Graph

Gmail’s Social Graph

The Problem

• Users do not explicitly maintain group listsUsers do not explicitly maintain group lists– Membership changes dynamicallyToo time consuming and tedious– Too time consuming and tedious

The Implicit Social Graph

• Billions of vertices – one for each emailBillions of vertices  one for each email address

• A group is a unique combination of one or• A group is a unique combination of one or more contacts with whom a user has interacted with in an email threadinteracted with in an email thread

The Implicit Social Graph

• Group membership represented throughGroup membership represented through edges

• An active user has on average 350 groups• An active user has on average 350 groups• Groups have a mean size of 6• Edges have direction and weight

Interaction Weight

• Iout = set of outgoing interactionsout g g• Iin = set of incoming interactions• t = current time• tnow = current time• t(i) = timestamp of interaction i• λ = half‐life of interaction weights

Use Cases

• “Don’t forget Bob”Don t forget Bob• “Got the wrong Bob?”

Restrictions

• Observability only of a user’s egocentricObservability only of a user s egocentric network

• Message contents are not included• Message contents are not included

Core Routine

• S = set of contactsS = set of contacts that make up the group to begroup to be expanded

• F = map of friend• F = map of friend sugges ons → confidence scoreconfidence score

UpdateScore Implementation #1

• Sums IR scores of all groups g pwith overlap to the seed

• Does not consider the d f i il it f thdegree of similarity of the seed group to candidate groups

• Biased toward groups with high IR, users in many groups

UpdateScore Implementation #2

• Scores weighted byScores weighted by similarity of the seed group to thegroup to the candidate group

UpdateScore Implementation #3

• Counts the numberCounts the number of groups a contact belongs to that havebelongs to that have any overlap with the seedseed

• Does not use IRBi d d• Biased toward users in many groups

UpdateScore Implementation #4

• Sum of IR scores forSum of IR scores for all the groups the candidate usercandidate user belongs to

• Biased toward• Biased toward frequently contacted usersusers

Evaluation

• Randomly sampled real email trafficRandomly sampled real email traffic• Removed traffic from suspected bots and inactive usersinactive users

• Sample a few contacts from each group for h dthe seed

• Measure ability of algorithms to account for the rest of the group

Results

Results

Results

“Don’t Forget Bob”

• Straightforward implementation of FriendStraightforward implementation of Friend Suggest algorithm

“Got the wrong Bob?”

• Iterate through eachIterate through each recipient

• Find similarly named• Find similarly named recipientsIf h i• If their score > current recipient’s, 

if hnotify the user

“Got the wrong Bob?”

Potential Applications

• Photo document sharing sitesPhoto, document sharing sites• IM communicationO li l d i i i• Online calendar invitations

• Comments on blog posts• Text messaging, phone activity

Future Work

• Study relative importance of differentStudy relative importance of different interaction types to determine social relationshipsrelationships

• Use of Friend Suggest to identify trusted usersR d ti– Recommendations

– Content sharing

Q&AQ&A