Suggesting Friends Using the Implicit Social Graph

Suggesting Friends Using the Implicit Social Graph

Maayan Roth1, Assaf Ben-David1, David Deutscher2, Guy Fisher1, Ilan Horn2, Ari Leichtberg2, Naty Leiser2, Yossi Matias1, Ron Merom1

1Google, Inc., Tel Aviv, Israel 2Google, Inc., Haifa, Israel

SIGKDD 2010

2010. 11. 01.

Summarized and Presented by Kim Chung Rim, IDS Lab., Seoul National University

Copyright 2010 by CEBT

Contents

Introduction

Problem Definition

Concept Definition

Goal

Various Score Measuring Algorithms

Experiment

Applications

Don’t Forget Bob!

Got the Wrong Bob?

Conclusion & Discussion

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Introduction

Group communication is prevalent

10% of e-mails are sent to more than one recipient, and 4% of e-mails are sent to 5 or more recipients.

Within enterprise domains, 40% of e-mails are sent to more than one recipient, and nearly 10% of e-mails are sent to 5 or more recipients.

User study show that they tend to communicate repeat-edly with the same groups of contacts.

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Problem Definition

However, users do not take the time to create and main-tain custom contact groups.

The work of ‘creating groups manually’ is tedious and time-consuming.

Even if users create contact groups, it is likely to change dynamically over time.

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Goal

The goal of this paper is to

Introduce the concept of Implicit social graph

Suggest a measurement to quantify interaction between users and contact group

Present a friend-suggestion algorithm that assists users in the creation of custom contact groups

Evaluate the friend-suggestion algorithm

Apply this novel friend-suggestion algorithm to practical use.

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Concept Definition – Implicit Social Graph

A graph, where

each node is an email address

each edge has weight and direction (incoming and outgoing mail)

each edge is a set of nodes (group of contacts)

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a directed weighted Hypergraph

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Concept Definition – Egocentric Net-work

Hypergraph composed of all the edges leading into or out of a single user node

No friend-of-friend hyeperedges are considered

Each hyperedge is defined as implicit group

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Concept Definition – Interactions Rank

Interactions Rank

A metric to compute the weight of hyperedge

The weight has to satisfy following criteria

Frequency

– groups with frequent interactions are more important

Recency

– Interactions Rank is dynamic over time

Direction

– Interactions that the user initiates are more significant than in-teractions that the user does not initiate

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Concept Definition – Interactions Rank

Interactions Rank (IR)

: the set of outgoing interactions

: the set of incoming interactions

: current time

: timestamp of an Interaction

: half-life

: relative importance of outgoing vs. incoming interac-tion

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Core Routine of Friend Suggest

Returns a set of scores for contacts

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S : a small set of contacts

G : a set of contact groups

g : a set of contacts with whom u has interactions

F : a set of scores for each contact [0,1]


Scoring Functions – base functions

Intersecting Group Count

Simply counts the number of groups that have intersection with the seed S and contains contact c at the same time.

Does not consider IR value of groups

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Scoring Functions – base functions

Top Contact Score

Sums up all the IR values of the implicit groups containing each contact

Ignores seed and always suggests the top-ranked contacts

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Scoring Functions

Intersecting Group Score

Sums up all the IR values of the implicit groups that have a non-empty intersection with the seed set and contains con-tact c at the same time

Finds all the context in which contact c exchanged emails or was a co-recipient with at least one seed group member

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Scoring Functions

Intersection Weighted Score

However, more contacts in g intersect with S means higher degree of similarity

Taking this intuition into account, Intersection Weighted Score returns IR multiplied with a constant k and the size of intersection of g and S

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Evaluation

Methodology

10,000 email interactions with between 3 and 25 recipients are randomly sampled

All sampled email interactions are interactions by active user

– A user who has minimum 5 implicit groups, sent at least one email within 7 days before sampled interaction

Each recipient list is a group of contacts that were implicitly clustered by the user

From that recipient list, few contact addresses are sampled and tested as seeds to see how well the rest addresses are recreated

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Evaluation metric

Precision & Recall

Precision is the percent of correct suggestions out of the to-tal number of contacts suggested for each seed group

Recall is the percent of correct suggestions out of the total number of email recipients who were not already members of the seed group

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Results

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Applications: Don’t Forget Bob!

Don’t Forget Bob uses the Friend Suggest Algorithm

Once user has added at least two contact addresses, that user’s egocentric network is fetched from the implicit social graph

Friend Suggest generates up to 4 contacts who best ex-pands the seed set of existing contacts.

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Applications: Got The Wrong Bob?

Got The Wrong Bob is implemented to fix the auto-com-pletion errors

For each contact in the current recipient list L, Wrong Bob excludes and builds a new seed set

When Friend suggest can restore , Wrong Bob stops to find a replacement

However, when cannot be restored, Wrong Bob searches for a replacement of

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Applications: Got The Wrong Bob?

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Conclusion & Discussion

Introduce implicit social graph and Interactions Rank

Define Friend Suggest Algorithm

Propose two applications of the Friend Suggest algorithm

Applicable to other types of communication

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Documents

Suggesting Friends Using the Implicit Social Graph