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MOPSI – Facebook(Social Network Analysis)

Chaitanya Khurana

27.1.2014

Version 3

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Table of contents

1. Facebook basics

1.1 Facebook components

1.2 Facebook page

1.3 Facebook group

1.4 Facebook app

2. Social Network Analysis

2.1 MOPSI-Facebook Network – a case study

2.2 Improving MOPSI’s recommendation using Facebook user data.

2.3 Advertising via MOPSI using Social Network Analysis

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1. Facebook basics

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1.1 Facebook components

1. Users

2. Pages

3. Groups

4. Apps

5. Events

User

Joins Event

App

Use

s

Group

Joins

Page Likes

Home

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1.2 Facebook page

Source: https://www.facebook.com/help/www/174987089221178

- People who like your Page and their friends can get updates in News Feed.

- Content of a page is public (visible to all).

- If a user wants to create a Page to represent a business, he must be an official representative of it.

Home

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1.3 Facebook group

- It is similar to forum/ discussion board.

- It is different from Page because it can have three privacy options: Open, Closed and Secret.

Source: https://www.facebook.com/help/162866443847527

Home

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1.4 Facebook app

1. Hosted on Facebook

2. Allows third party applications (e.g. MOPSI) to access FB data

Display Name

Namespace

Facebook app URL

Home

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MOPSI-FB app: App Id and Secret

MOPSI-FB app URL

- App Id is unique for each app

- App Id and secret are used in Login Dialog generation.

- Login Dialog helps in access token generation.

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Introduction: Access Token & Permission

Access token: It is a string that uniquely identifies a user and used by app to access his data. Each has certain permissions.

e.g. CAABtc4NJzc1D35FKIENvjdnvreG…

Permissions: These are specified at the time of Login Dialog generation.

e.g. user_friends, user_photos, email etc.

FacebookGraphAPI

MOPSI

CAABtc4NJzc1D…

User’s FB Data (email)

(JSON Format) CAABtc4NJzc1D…

User’s Access Token

Permissions: email

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Access Token generation

User gets a Login Dialog.

User authenticates and approves permissions

Access token is returned to client.

Access Token generation workflow

The token is then saved in MOPSI database

User requests accessto MOPSI using

Facebook

Graph API generates Login Dialog.

MOPSI Facebook app

MOPSI server

App idFacebook Graph API

App secret

Request* send to

generate Login Dialog

* Request includes app id, app secret and permissions.

App id e.g. 123568456App secret e.g. 236659

Permissions: email, user_photos, user_friends.

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Access Token expiration

Access token expires in 3 conditions:

- The lifetime (60 days) of access token is over.

- The user changes the Facebook password.

- The user de-authorizes our app.

Note: If we modify the permissions in the application to get additional data, we need the updated access token.

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Web solution for expired tokens

When a user presses ‘f’ button to publish photo/route and if access token is expired,a pop up comes and ask user to connect MOPSI account with Facebook.

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Mobile solution for expired tokens

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Permissions for Access Token

Categories of permissions:

(a) User Data Permissions (UDP)

(b) Friends Data Permissions (FDP)

(c) Extended Permissions (EP)

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User Data Permissions

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Friends Data Permissions

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Extended Permissions

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MOPSI-FB permissions

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Comparing permissions with othersPermission MOPSI-FB Tripadvisor Foursquare

Basic informationEmailBirthdayUser’s profile infoPhotosStatus updatesFriend’s profile infoPhoto updates shared with userStatus updates shared with userPost on your behalfTotal

YesYesNoYesYesNoNoNoNoYes5

YesYesNoYesYesYesYesYesYesYes9

YesYesYesYesYesYesNoYesYesYes9

Note: Profile info includes: Educational history, Likes, Hometown and Location

Back -> User similarity slide

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System Diagram of MOPSI-FB interaction

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MOPSI-FB features

- Registration

- Authentication

- Publish photo on Facebook

- Publish route on Facebook

- MOPSI-FB Network

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Registration via Web

Create a new account in MOPSI using

Sign up with Facebook button.

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Facebook Login

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Login dialog asking User’s permissions

Popup generated by Facebook. If user presses button “Okay”,Facebook allows MOPSI to read user data

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Extended permissions

If a user presses button “Okay”, he is redirected to MOPSI website.

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MOPSI Home page

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Registration via mobile

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Authentication

Sign in with Facebook button allows user to log in to MOPSI using Facebook credentials.

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Connect with Facebook workflow

If user is logged in

Facebook?

YESUse Facebook SDK to pop up a login to Facebook window

NO

Is login to Facebook

successful?

NO

YES

Facebook SDK displays error message in popup window

Creates MOPSI username, password and send these by email. Save user details in MOPSI db.

Is user’s Facebook id &

email available in MOPSI db?NO

Updates Facebook name, access token andfacebook friends.

YES

Redirect to MOPSI webpage (in case of web)Start MOPSI application (in case of mobile)

User presses Sign in/ Sign up with Facebook button

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Publish photo on Facebook

Welcome screen of MOPSI mobile application

Camera optionto capture photo

Settings screen

Tick checkboxto share photoson Facebook.

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MOPSI user’s photo on MOPSI

Mopsi user’s photo on

Map in MOPSI

Location of photo taken

Publish to Facebook option

Description of photo

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MOPSI user’s photo on FacebookDescription Location taken automatically

by MOPSIThis link redirects to MOPSI which allows us to see photo on Map.

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MOPSI Photo album on Facebook

User’s profile picture

Photo Album links to Mopsi

Sample picturesof latest additions

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Photo publishing workflowUser presses ‘F’ button to publish

photo

Get user’s access token from MOPSI database

Is access token valid?

YES NOGet user’s Facebook

album id from database

Is album id valid?

YES

If total photos

in album < 200?

YES

Add new photo in current album.

NO

NO

Create new photo album in Facebook. Save album id in

database

Add new photo in newly created album.

Access Token expiration handling

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Publish route on Facebook

Welcome screen Tracking screen

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The route on MOPSI

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MOPSI user’s route shared on Facebook

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Route publishing workflowUser presses ‘F’ button to publish

route

Get user’s access token from MOPSI database

Is access token valid?

YES NOGet all location points

of user w.r.t. timeAccess Token

expiration handling

Calculate bounds, start & end point and

distance

Get route summary: start & end route address,

duration, mode, speed, distance, date etc.

Create route image by providing route points and bound values to OSM or Google API.

Add route photo on server. Call Facebook Graph API to

publish route on user’s Facebook wall.

Home

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2. Social Network Analysis

Home

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2.1 MOPSI-FB Network – a case study

MOPSI- FB Network is depicted using solid lines between MOPSI-FB users and their FB friends.

Home

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Introduction

MOPSI has two types of users:- MOPSI users: who register directly on MOPSI.- MOPSI-FB users: who register on MOPSI via FB.

The third type of users are:- Facebook users: who are Facebook friends of

MOPSI-FB users.

Note: MOPSI users can become MOPSI-FB by pressing Connect to

Facebook button available in profile page on MOPSI.

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Friendship links

There are two types of Friendship links:

Direct link (link between a MOPSI-FB user and his FB friend)

Friend-Friend link (link between two FB friends of a MOPSI-FB user)

- In this example, ‘Chaitanya’ is a MOPSI-FB user. Others are his FB friends.

- Link between Chaitanya & Andrei isa Direct link.

- Link between Andrei & Radu is aFriend-Friend link.

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Friendship network extraction

Facebook allows us fetching friends who are up to 1 degree of separation.

Example –

Chaitanya is a MOPSI-FB User

- Green nodes (1 degree away)

can be fetched.

- White nodes (2 degrees away)

can’t be fetched.

C is friend of Andrei

A is friend of Mikko

B is friend of Radu

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Basic network operations

1. Layout – It deals with the arrangement of nodes while graph is rendering.

2. Average degree – It is the average number of neighbours of all nodes.

3. Betweenness Centrality – It measures how often node appears on shortest paths between nodes in the network.

4. Modularity – It determines modules or communities in which nodes are densely connected internally.

5. Giant component – It is the largest connected component in the graph.

6. Ego network – It is the network of an ego or actor node which consists of it’s direct connections with other nodes.

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Practical Example1: Chaitanya’s Facebook Network

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Operation 1 – Layout

ForceAtlas 2 Layout

ForceAtlas2 is a layout algorithm whichallows to manipulate the graph whileit is rendering.

Scaling: 30.0

Scaling is the amount of repulsion we want between nodes in the graph. Moremakes a more sparse graph.

This algorithm is good enough to deal with very small graphs (10 nodes) to mid-size graphs (10, 000 nodes). It can even dealwith bigger graphs, but consumes more time.

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Operation 2 – Average Degree

Chaitanya Khurana

Average Degree: 3.314

Size of node varies according to node degree.

Min Size: 20Max Size: 80

The average degree of a network is computed over the degree of all nodes, i.e. the average number of neighbours of nodes.

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Operation 3 – Modularity

Modularity or Community Structure is a Community detection algorithm. Community: The nodes of the network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally.

Algorithm used: Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre, Fast unfolding of communities in large networks, in Journal of Statistical Mechanics: Theory and Experiment 2008 (10), P1000

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Modularity (Communities - 7)

Green - College Friends/ Teachers

Pink – Friends whom I don’t know

or never seen outside Facebook

Blue – Friends of Finland/ UEF

Yellow - Friends of school

Red - Friends of Political Party

Different colour - My relatives

Purple – Neighbours

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Observation

A new community is formed if any change

in:- Work place (includes school, office etc.)- Place of living

Challenge: Exactly determine communities.

Could be useful:- To measure user similarity of users in same

community versus different community.- To find communities of all susceptible members. Does users having susceptible

members in different communities tend to be more influential than in same.

- Users who have high degree than less degree within community.

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Operation 4 – Ego Network

Ego Network - Ego networks consist of a focal node ("ego") and the nodes to whom ego is directly connected to (these are called "alters") plus the ties, if any, among the alters.

Alters

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Example of Ego Network

Node ID: 1373260832 (Gurvinder Singh) – Focal node

Depth: 1

Applying operationof Ego Network

Complete GraphEgo Network of Focal node:Gurvinder Singh

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Example continues..

Node ID: 1373260832 (Gurvinder Singh)

Depth: 1

Connected to one of my college friend in

green.

At Depth 2, he is connected to everyone.

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Practical Example2: MOPSI-FB Network (old)

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Operation 1 – Layout (ForceAtlas 2)

Each node represents a “user”

Mopsi userin centre

Facebook friends ofMopsi user

Mopsi user with no Facebook friend

MOPSI-FB users: 178Facebook users: 4792Friendship links: 7651

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Nikola ManojlovicFB Friends: 715

Alexandra JakovljevićFB Friends: 282

AleksiFB Friends: 54

Jesika MatysikFB Friends: 79

Iulian MariusFB Friends: 82

Chandan ShahiFB Friends: 302

Monika ScheffernFB Friends: 153

Pasi FräntiFB Friends: 68 Oili Kohonen

FB Friends: 275

ChaitanyaFB Friends: 534

Sami PietinenFB Friends: 88

Radu FB Friends: 239

TerezaFB Friends: 26

KarolFB:363

MariolaFB:151

AdamFB Friends: 110

KeytiannyFB Friends: 42

ZhentianFB Friends: 94

Ding LiaoFB Friends: 124

Hao Chen FB Friends: 141

Mopsi users

FB usersOperation 2 Degree (1.595)

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Operation 3 – Betweenness Centrality (Brokers)

It measures how often node appears on shortest paths between nodes in the network.

C has highest Betweenness centrality : 4

B & D have betweenness centrality 3

A & E has 0 betweenness since there is no shortest path which passes from A and E.

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Chaitanya

Radu Mariescu- Istodor

Nikola Manojlovic

Chandan ShahiVlad Manea

Oili Kohonen

Karol

Operation 3 – Betweenness Centrality (Brokers)

HighMediumLow

(Betweenness Centrality)

Network Diameter: 5Number of shortest paths: 6,412,170

Betweenness Centrality

HighChaitanya: 1,110,163Radu: 940,253MediumKarol: 707,895Chandan Shahi: 606,999

Oili Kohonen: 546,566Vlad Manea: 586,404

Nikola Manojlovic: 456,603

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Operation 4 – Modularity (Communities) Different colour represents differentcommunities.

Total Communities: 131Mopsi user (Nikola) with his Facebook friendscomes under one community.It is the biggestCommunity with715 friends.

This Mopsi user has zeroFacebook friend. So, he is theonly member in his community.It is one of the smallest communitiesin the graph.

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Analysis of Communities

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Pasi Franti

Oili Kohonen

Chandan ShahiVlad Manea

Chaitanya

Karol

Radu Mariescu-Istodor

Sami Pietinen

Hao Chen Ding Liao

Zhentian Wan

Operation 5 – Giant ComponentNodes: 2286 (47.78%)Edges: 5012 (67.8%)

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Operation 6 – Ego Network (of any node in the network)

Node ID: 13 (Pasi Franti) – Focal NodeDepth: 1Connected to 69 nodes i.e. 1.44% of total nodes

Pasi Franti

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Operation 6 – Ego Network (of any node in the network)

Node ID: 13 (Pasi Franti) – Focal NodeDepth: 2Connected to 609 nodes i.e. 12.73% of total nodes

Pasi Franti

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Operation 6 – Ego Network (of any node in the network)

Node ID: 13 (Pasi Franti) – Focal NodeDepth: 3Connected to 2004 nodes i.e. 41.89% of total nodes

Note: Even at 3 degrees of separation,Pasi node (having lower BetweennessCentrality ) could not reach the value of Giant component (47.78%)

But, when I compared with Radu node(having highest Betweenness centrality)it could reach the value of Giant component(47.78%)

Home

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2.2 Improving MOPSI’s Recommendation using Facebook user data.

Home

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Introduction

Facebook user data includes: Profile information of user

Likes, comments and tags on photos and status updates.

GenderBirthday (age)FriendsCheckinsRelationship statusPhotos

LocationEducational historyStatus updateWork historyLikesEmail

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Data accessible by MOPSI FB app

User data (if public)

Likes and comments made by a user and his friends on user’s photo. Photo tags by user’s friend.

User’s friend data

Gender

GenderFriendsPhotosEmailLikes

Work historyEducational historyRelationship statusLocation

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User Similarity

Users & their friends’ information could be useful for user similarity. These include:

- hometown & current city

- work history

- likes & comments on user’s photos

- locations of photo taken

- checkins (With Latitude and Longitude)

- likes & comments on wall posts

Green shows information retrieval possible with current permissions. For others, we need additional FB permissions.

Permissions (general) used by other companies.

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User Similarity – Likes & Comments on photos

Karol Yuliya- In this example, we consider 5 users in the set of MOPSI-FB users and 1 Facebook photo.

Goal: Find Similar users.

Result: Karol and Yuliya are similar users.

This is basic example. In reality, we have thousnadsof photos and hundreds of MOPSI-FB users.

Open questions:- Can we say all these (Radu, Karol & Yuliya) are similar? Radu- uploaded. Others – Likes.- Can we say Karol, Yuliya and Andrei are similarbecause they took action on photo.

Zhentian

Set of MOPSI-FB users

Radu Andrei

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User Similarity – Location of photo (outside MOPSI)

- Location of photo tells user has been to the specific place.

- Similarity of users may also consider the previous visited places of all users. See next slide.

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Simple example – Previous photo locations

Tallinn, Estonia (3)Lappeenranta, Finland (1)Petrozavodsk (2)Düsseldorf, Germany (2)Aachen, Germany (3)Vaals (NL) (1)Kelmis,Belgien (1)Joensuu (4)

Joensuu (11)Region Stavanger (1)Bergen, Norway (1)Itlay Venice (1)

Kuopio, Finland (15)Kitee (10)

Colegiul National (1)Madame Tussauds London (1)Tour Eiffel (1)Brussels, Belgium (1)Amsterdam, Netherlands (1)Frankfurt am Main (2)Freiburg, Germany (1)Geneva, Switzerland (1)Monaco (1)Nice, France (1)Genova, Italy (1)Colosseo (3)Maria Theresia Wien (1)

Tallinn, Estonia (1)Joensuu (24)

Karol YuliyaZhentian Radu Andrei

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Scoring and similarity

U = {U1 , U2 , U3 …….Un }; It is the set of MOPSI-FB users.

L = {L1 , L2 , L3 ………Ln }; It is the set of all photo locations of a

MOPSI-FB user.

)( #

)( #),( 1

11 Lphotos

LphotosLUScore

Equation 1:

} ), ( ),, ( min{ w.r.t.) , ( 1211121 LUScoreLUScoreLUUscoreSimilarity Equation 2:

Source: M.J. Lee, C.W. Chung, “A User Similarity Calculation Based on the Location for Social Network Services”, in DASFAA 2011, Part I, LNCS 6587, pp. 38–52, 2011

where #: Total

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Similarity score example

For example, Location: Tallinn, Estonia

Score (Karol, Tallinn) = 3/17 = 0.176

Score (Andrei, Tallinn) = 1/25 = 0.04

Similarity score (Karol, Andrei) w.r.t. Tallinn =

min (0.176, 0.04) = 0.04

These can be calculated for all locations to find resultant

similarity score.

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User Similarity – Check-ins

Facebook Check-in

- Check-ins are in general more specific than location of photo as we saw inprevious example. In this example, Bukhara is an Indian restaurant in Cape Town.

- Rest of procedure for finding user similarity is same as previous example.

- The only hindrance is we need to add one permission of ‘user_status’

Home

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User Similarity – Page Likes

ZhentianChaitanya

category: Movie, name: December Boys

category: Author, name: Chetan Bhagat

(and so on..)

Total = 148

category: Travel/leisure,name: Norwegian.com/fi

category: Local business, name: Zakka

(and so on..)

Total = 9

List of Similar Pages

category: Universityname: University of Eastern Finland

category: Attractions/Things to doname: Mopsi

And so on..

Total similar pages = 4

Similarity Score = min (4/148, 4/9)= (0.027, 0.44)= 0.027

) U

U ,

U

U (min

2

12

1

12

PagesTotal

PagesSimilar

PagesTotal

PagesSimilarScoreSimilarity

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User Profile - Categories of Pages

Chaitanya

Pages I likecategory: Movie = 11 (7.43 %) category: Author = 2 (1.35 %)category: Politician = 7 (4.72%)And so on..

These figures convincingly ask to recommend:

Cinema Halls than Book shops and Music storesin the results of Points of Interest.

Categories like:

Movies, TV shows can be clustered in Movies.Book and Author can be clustered in Book.Musician/Band, Music can be clustered in Music.

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User Similarity - Categories of Pages

Chaitanya

Andrei

Total Pages Andrei Like = 148category: Author = 2/148=0.13category: Book = 0/148=0

Total Pages Andrei Like = 203category: Author = 0/203 = 0category: Book = 11/203 = 0.054

Similarity score (Chaitanya, Andrei) in Book Store = min (0.13,0.054)= 0.054

Similarity score for Music Store, Restaurant etc can be computed keeping Services in mind.

Also, people who like pages of books more indicate that they love Reading and may be ready to lend books on demand.

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2.3 Advertising via MOPSI using Social Network Analysis

Home

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Introduction

1. The advertising idea for MOPSI makes use of

analysis of MOPSI-FB network.

2. It involves analyzing data sharing to determine relationship strength.

3. This could be useful to identify Influential users.

4. These users may help us in promoting products.

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Motivation

1. New Advertising Model which makes use of Social Network Analysis.

2. Revenue generation for MOPSI

3. MOPSI users (connected via MOPSI) will get discounts

4. MOPSI will become more popular and get more

users.

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Stakeholders involved

2. Partner Companies e.g. Restaurants, Clubs, Stores etc..

1. MOPSI application

Ex: ABC Restaurant

3. MOPSI-FB users

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Working (MOPSI users)

Pasi

Influential MOPSI-FB user

Pasi visited ABC restaurant

- Pasi took photo and uploaded on FB- Got 10% discount

Photo contains:Product, Company’s &MOPSI logo

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Working (Company)

-Wants to promote Pizza-Asks MOPSI about Influential users

MOPSI-Facebook app finds:1. Influential users2. Their Susceptible friends Pasi

Influential user

Pasi’s Suscetible Friendson Facebook

MOPSI Business Interface

(explained on next slide)

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MOPSI Business Interface

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Influence & Susceptibility

Influence – capacity to have an effect on the behaviour of someone.

Susceptibility – likely to be influenced by other.

This picture was uploaded on Twitter byBarack Obama (Influential Twitter user).As a result, it became viral on it.

Influential user: Barack ObamaSusceptible users: All who re-tweeted.

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Influence: Social search

)),(Re

),(Re..(

),(

),( ),(

2

12

2

1221

XX

XUtweets

UUtweetsge

XUInvests

UUInvestsUUInfluence

Influence (U1, U2) is defined as proportion of U2’s investment on U1. Invests (U2, U1) is the investment U2 makes on U1. Reference

User U1 User U2

Influence

Invests

X

XUInfluenceUInfluence ),()( 11

Influence of user is defined as the sum of the influences the user has on others.

Source: All Friends are not equal: Using Weights in Social Graph to Improve Search

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Purpose: Strongest path

31

3

4

5 1

22

11

1

13

1

1 1

3

4

5

1

21

1

3

1

1

1

2

Edge = Invest (Retweets)

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1

A (Start)

B

C

DE

F

G

H (Target)Strongest Path: (A,D,F,H) = 2.0 * d

3B

C

D

A

I(A,D) =3/6 =0.5 I(A,B) =4/10=0.4

I(A,C)=0.33

I(B,H)=0

H

I(C,H)=0.25

I(D,F) = 0.75

F

I(D,F) = 0.75

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Algorithm

1. Each edge gets some weight.2. Select start node and target node 3. Find shortest path using Djikstra’s algo.4. Calculate Influence of each edge.5. Calculate Strength S(P) of all paths.6. S(P) = Π (D*Influence (ei)), ei belongs to P. 7. Path with highest strength is strongest path.

D (decay or discount factor) = 0.95 (found experimentally)

Reference: S. Hangal, D. MacLean, M.S. Lam, J. Heer. All Friends are not equal – Using Weights in Social Graphs to Improve Search. SNA - KDD Workshop 2010, Washington D.C, USA.

Web link: http://xenon.stanford.edu/~hangal/socialsearch/

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Influence: Comparing users

Value of c is determined based on this reference: C. Wittman (2011). Comments 4x More Valuable Than Likes. Available: http://edgerankchecker.com/blog/2011/11/comments-4x-more-valuable-than-likes/. Last accessed 7th Jan 2013.

Edge Influence (U1, U2) is defined as U2’s investment (likes and comments) on total photos of U1.

User U1 User U2

Influence

likes & comments

X

XUInfluenceEdgeUInfluenceNode ),( )( 11

)(U

))(**)(U(),(

1

12 2

21 k

UCncLUUInfluenceEdge ktoi

iii

where:k– total photosLi – likes (0 or 1)c – value is 4ni – number of commentsCi – comments (0 or1)

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Purpose: Influential users

Influence value

Low High

1 21

Edge Influence:24 3

Node Influence: 4

11

Node Influence: 9

Node Influence: 8

1

1

11

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1

Node Influence is calculated usingSum of Edge Influences.

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Comparing Influence Equations

40 10

25 30

Total Photos = 100N.I (A)=(40/95)+(10/25)

= 0.8210N.I (A)=(40/100)+(10/100)

= 0.5

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Total photos = 80N.I (B)=(40/70)+(15/95)

= 0.729N.I (B)=(40/80)+(15/80)

= 0.687540 15

3515 30

N.I (A) is Node Influence . In this, influence = ),(

),(

2

12

X

XUInvests

UUInvests

N.I (B) is Node Influence. In this, influence =)&(/)(U

))(**)(U(

1

12 2

CLTotalValk

UCncLktoi

iii

A B

Likes

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Disadvantages

1. Assume each node has equal, fixed resources to invest.

2. It requires global view of the data.

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Value of ‘c’

- EdgeRank Checker examined a random sampling of 5,500+

Facebook Pages.

- They analyzed 80,000+ of their posts over the month of

October (2011).

- These posts were all of the “Link” type, to keep Clicks more

accurately represented in our data.

Result:

Average clicks per Like: 3.103

Average clicks per Comment: 14.678

Value of 1 Comment = Value of 4 Likes.

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Determining Susceptibility

),( ),( 2112 UUInfluenceEdgeUUlitySusceptibi

Susceptibility of a user (U2) with respect to other user (U1) is determined as follows:

Similarity based on Influence.Pasi ’s Susceptible users: Andrei, Zhao etc.Radu ‘s Susceptible users: Andrei, Karol etc.

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Comparison with existing solution

Proposed advertising model

• Publish photo of product on Facebook if a user buys and gets immediate discount.

Advantage

- Company does not give any discount for just visiting.

Foursquare’s advertising model

• Publish checkin on Facebook even if you don’t buy anything.

Disadvantages

- User will get points, badges and can even become Mayor even without buying anything

.

- Company has to give discount even to such Mayors.

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Comparison with existing solution

Proposed advertising model• Uses Social Network Analysis to

find Influential members.

• Photo will contain product, company’s and MOPSI logo.

Foursquare’s advertising model

• No such initiative is done

• Check in just tells about the place. No information about product.

Home

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Thank You!