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.
FACTS AND STATISTICS COLLECTED TOGATHER FOR REFERENCE ANALYSIS.
THE QUANTITIES ,CHARACTERS ,SYMBOLS ON WHICH OPERATIONS ARE PERFORMED BY A COMPUTER, BEING STORED AND TRANSMITTED.
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.
INFORMATION CAN BE CONVERTED INTO KNOWLEDGE ABOUT HISTORICAL PATTERNS AND FUTURE TRENDS.
FROM THE LARGE DATA SET FIND THE:
THE OVERALL ROLE OF DATA MINING IS TO EXTRACT INFORMATION FROM THE DATA SET AND TRANSFORM IT INTO AN UNDERSTANDABLE DATA FOR FURTHUR USE
THE PROCESS OF COLLECTING,SEARCHING THROUGH AND ANALYSING A LARGE AMOUNT OF DATA IN A DATABASE , AS TO DISCOVER PATTERNS 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 ANALYSIS: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
COMMUNITY-BASED OUESTION ANSWER( C-QA)
EMAILS AND CHAT
MEDIA SHARING,OPINION VIEWS AND RATINGS
Data mining technique in social media
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) tend to 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 is used for understanding relationship as well as content.
Phone provider can look at phone call records using graph mining.
Example of graph mining in Facebook :
Query example: “Restaurants in Pune liked by friends”
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.
Each individual or team is shown as a circular nodeon the diagram. Numbered for ease of reference.
4. The nodes may each have additional connections outside of the task network identified.
Nodes 1, 3, 4, 5, 6, 8, 11, 14 are the most peripheralwith the least connections.
There are two cliquesin 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 has the most connections and therefore the highest degree centrality.
Example of graph mining from Facebook
Sample query for graph search
Result for graph search
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 of various forms.
For example a social network may contain blogs, articles , messages etc.
Text mining process
The data collector module continuously downloads data from one or more social platform and stores raw data into the database. Based on application type the parameters are specified with the API call.
This is the process used to define and analyse the data requirements needed to support the application process within the scope of corresponding applications.
Mining methods(Text mining)
1.) Clustering Analysis: Automatic or semi-automatic analysis of large quantity of data to extract previously unknown interesting patterns such as groups of data records known as cluster analysis.
2.)Anomaly detection: It’s the search for items or events which do not confirm to an expected pattern.
data from Facebook
Facebook platform provides API,SDK for developing applications which access the Facebook data. The Facebook SDK provides a fast native, Facebook integration ,using the exact same implementation regardless of which environment you are deployed to.
In mobile Facebook provides SDK for:
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.
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.
Applications of Social Network Analysis
If they are understood ,better relationships and knowledge flows can be measured, monitored, and evaluated, perhaps (for instance) to enhance organizational performance
Identify individuals, teams, and units who play central roles.
Discern information breakdowns, bottlenecks, structural holes, as well as isolated individuals, teams, and units.
Make out opportunities to accelerate knowledge flows across functional and organizational boundaries.
Applications of Social Network Analysis
Strengthen the efficiency and effectiveness of existing, formal communication channels.
Leverage peer support.
Improve innovation and learning.
Limitations of sna
Connections may sometimes may not depict correct hierarchy.
SNA does not show the effectiveness or quality of the relationships between people. Some connections may be more productive than others. 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 control
SOCIAL MEDIA : BIG, RICH AND OPEN DATA
-BILLION USERS,BILLION CONTENTS
-BILLIONS OF CONNECTIONS
-LARGE –SCALE NETWORK