data mining in social network
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data mining in social networkTRANSCRIPT
7/18/2019 Data Mining in Social Network
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DATA MINING IN SOCIAL
NETWORK
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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.
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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.
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DATA,INFORMATION& KNOWLEDGE
KNOWLEDGE:
INFORMATION CAN BE CONVERTED INTO KNOWLEDGE ABOUT
HISTORICAL PATTERNS AND FUTURE TRENDS.
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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.
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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
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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.
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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
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DATA MINING TEChNIquE
IN SOCIAL MEDIA
GRAPh
MINING
TEXT MINING
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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.
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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
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GR Ph DEFINITION
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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
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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
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APRIORI-BASED APPROACh
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PATTERN-BASED APPROACh
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EXAMPLE OF GRAPh MININGFROM FACEBOOK
Sample query for graph search Result for graph search
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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.
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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.
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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.
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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#
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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.
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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.
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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.
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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.
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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
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CONCLuSION
SOCIAL MEDIA : BIG, RICH AND OPEN DATA
-BILLION USERS,BILLION CONTENTS
-TEXTUAL MULTIMEDIA
-BILLIONS OF CONNECTIONS
CHALLENGES:
-LARGE – SCALE NETWORK
-NOISE
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ThANKYOu