Data Mining in Social Network

7/18/2019 Data Mining in Social Network

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DATA MINING IN SOCIAL

NETWORK



CONTENTS

DATA, KNOWLEDE,INFORMATION

DATA MINING

SOCIAL NETWORK,SOCIAL NETWORK ANALYSIS

DATA MINING IN SOCIAL NETWORKS: 1. GRAPH MINING.

2. TEXT MINING

ACCESSING DATA FROM FACEBOOK

APPLICATIONS OF SOCAIL NETWORK ANALYSIS

LIMITATIONS OF SOCIAL NETWORK ANALYSIS.



DATA,INFORMATION& KNOWLEDGE

DATA:

FACTS AND STATISTICS COLLECTED TOGATHER FOR REFERENCE

ANALYSIS.

THE QUANTITIES ,CHARACTERS ,SYMBOLS ON WHICH OPERATIONS

ARE PERFORMED BY A COMPUTER, BEING STORED AND TRANSMITTEDINFORMATION:

THE PATTERNS, ASSOCIATIONS,RELATIONSHIP AMONG ALL THESE

DATA CAN PROVIDE INFORMATION.FOR EXAMPLE ANALYSIS OF SALE

TRANSACTION DATA CAN GIVE INFORMATION ABOUT WHICH

PRODUCTS ARE SELLING WHEN.



DATA,INFORMATION& KNOWLEDGE

KNOWLEDGE:

INFORMATION CAN BE CONVERTED INTO KNOWLEDGE ABOUT

HISTORICAL PATTERNS AND FUTURE TRENDS.



DATA MINING

FROM THE LARGE DATA SET FIND THE:

USEFUL

UNKNOWN

INFROMATION.

THE OVERALL ROLE OF DATA MINING IS TO EXTRACT INFORMATIONFROM THE DATA SET AND TRANSFORM IT INTO AN UNDERSTANDABLEDATA FOR FURTHUR USE

THE PROCESS OF COLLECTING,SEARCHING THROUGH AND ANALYSINGA LARGE AMOUNT OF DATA IN A DATABASE , AS TO DISCOVERPATTERNS AND RELATIONSHIPS.



A social network is a social

structure between actors

mostly individuals or

organizations

It indicates the ways in which

they are connected through

various social familiarities

ranging from casual

acquaintance to close familiar

bonds

SOCIAL NETWORK



SOCIAL NETWORKANALYSIS:DEFINITON

SOCIAL NETWORK ANALYSIS

FOCUSES ON THE STRUCTRE OF

RELATIONSHIP AMONG A SET OF

ACTORS.

Social network analysis maps and measures formal and

informal relationships to identify what facilitates or impedes

the information and knowledge flows that bind interacting

units, viz., who knows whom and who shares what information

and knowledge with whom through what media.



SOCIAL MEDIA PLATFORM

BLOGGING

MICROBLOGS

COMMUNITY-BASED OUESTION ANSWER( C-QA)

EMAILS AND CHAT

HYBRID APPLICATIONS WIKIS

SOCIAL NEWS

SOCIAL BOOKMARKING

MEDIA SHARING,OPINION VIEWS AND RATINGS



DATA MINING TEChNIquE

IN SOCIAL MEDIA

GRAPh

MINING

TEXT MINING



GRAPh MINING

1.)Graph mining:

Graphs(or networks) constitute a prominent data structure and

appear essentially in all form of information . Example include the web

graph ,social network. Typically, communities correspond to , group

of nodes , where nodes within the same community ( or clusters) tendto be highly similar sharing common features ,while on the other hand

nodes of different communities show low similarities.

Extracting useful knowledge (patterns, outliers ,etc) from structured

data that can be represented as graph.



GRAPh MINING

• Graph mining is used for understanding relationship

well as content.

• Phone provider can look at phone call records using

mining.

Example of graph mining in Facebook :

Query example: “Restaurants in Pune liked by frien



GR Ph DEFINITION



2.The lines linking each node

denote the relationships and

interaction between them in

order to complete the task.

3. Node 13 represents the

webpage and node 14

represents the target

audience for the webpage.

1. Each individual or

team is shown as a

circular node on

the diagram.

Numbered for ease

of reference.

4

ou

ne

N

NETWORK DIAGRAM



There are two cliques in the

network where all nodes are

connected to each other: 7-9-10

and 10-12-13.

The nodes with

more links show

who is well

connected in the

network

Node 7 h

conn

therefore

degre

NETWORK DIAGRAM



APRIORI-BASED APPROACh



PATTERN-BASED APPROACh



EXAMPLE OF GRAPh MININGFROM FACEBOOK

Sample query for graph search Result for graph search



TEXT MINING

2.)Text mining:

It is an emerging technology that attempts to extract

meaningful information from unstructured textual data.

Text mining is an extension of data mining to textual data.

A social network contains a lot of data in the nodes ofvarious forms.

For example a social network may contain blogs,

articles , messages etc.



TEXT MINING PROCESS

Data collection:

The data collector module cont

downloads data from one or mo

and stores raw data into the dat

application type the parameters

with the API call.

Data Modelling:

This is the process used to defi

the data requirements needed

application process within the s

corresponding applications.



MINING METhODS(TEXT MINING)

MINING METhODS:

1.) Clustering Analysis: Automatic or semi-automatic a

of large quantity of data to extract previously unknown int

patterns such as groups of data records known as clusteranalysis.

2.) Anomaly detection: It’s the search for items or even

do not confirm to an expected pattern.



ACCESS DATA FROM FACEBOOK

Facebook platform provides API,SDK for developing applications which ac

Facebook data. The Facebook SDK provides a fast native, Facebook integr

exact same implementation regardless of which environment you are deplo

In mobile Facebook provides SDK for:

1. iOS platform2. Android platform.

For web development SDK are provided by both Facebook and

community:Php, JavaScript ,ruby,node.js, C#



FACEBOOK API

Search API: The graph API is a simple HTTP based API that

gives access to the Facebook social graph, uniformly represented

objects in graph and connection between them.

FQL: Facebook Query Language enables you to use a SQL type

interface to query the data exposed by the graph API.

Dialogs: Facebook offers a number of dialogs to a Facebook

Login, posting a person’s timeline or sending requests.



FACEBOOK API

Chat: One can integrate a Facebook chat into a web-

based desktop or mobile instant messaging products.

Ads API: This allows you to build your own app as a

customized alternative to the Facebook ads.

Public feed API: This lets you read a stream of public

comments that have been posted.



PPLIC TIONS OF SOCI L NETWORK

N LYSIS

If they are understood ,better relationships and knowledge flows c

measured, monitored, and evaluated, perhaps (for instance) to en

organizational performance

Identify individuals, teams, and units who play central roles.

Discern information breakdowns, bottlenecks, structural holes,

isolated individuals, teams, and units.

Make out opportunities to accelerate knowledge flows across fu

organizational boundaries.



PPLIC TIONS OF SOCI L NETWORK

N LYSIS

Strengthen the efficiency and effectiveness of existing

formal communication channels.

Leverage peer support.

Improve innovation and learning.

Refine strategies.



LIMITATIONS OF SNA

Connections may sometimes may not depict correct hierarchy.

SNA does not show the effectiveness or quality of the relations

between people. Some connections may be more productive th

But sometimes such connections are not considered.

SNA does not show breakdowns in communication or barriers

In many cases the graphs are large scale hence difficult to con



CONCLuSION

SOCIAL MEDIA : BIG, RICH AND OPEN DATA

-BILLION USERS,BILLION CONTENTS

-TEXTUAL MULTIMEDIA

-BILLIONS OF CONNECTIONS

CHALLENGES:

-LARGE – SCALE NETWORK

-NOISE



ThANKYOu

Documents

Data Mining in Social Network