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Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks

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Published on February 20, 2009

Author: ACMBangalore

Source: slideshare.net

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Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks- Talk by Dr Jai Ganesh, SETLabs, Infosys at Search and Social Platforms tutorial, as part of Compute 2009, ACM Bangalore
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Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks Dr. Jai Ganesh Web 2.0 Research Lab

Overview Social Networks Social Network Analysis (SNA) SNA in Web 2.0 scenarios Why Invest in SNA Examples Example 1: Customer Service Operation Example 2: Organisational Network Analysis Example 3: Criminal Investigation Analysing Data Tools and Products Issues Conclusion

Social Networks

Social Network Analysis (SNA)

SNA in Web 2.0 scenarios

Why Invest in SNA

Examples

Example 1: Customer Service Operation

Example 2: Organisational Network Analysis

Example 3: Criminal Investigation

Analysing Data

Tools and Products

Issues

Conclusion

Overview of Web 2.0

Web 2.0: Overview Web 2.0 is about harnessing the potential of the Internet In a more collaborative and peer-to-peer manner Users communicate and collaborate while at the same time contribute and participate Is shaping the way you work and interact with information on the web Mindset change towards collaborative participation Shifts the focus to the user of the information User can search, choose, consume and modify the relevant content Web 2.0 refers to the adoption of open technologies and architectural frameworks to facilitate participative computing

Web 2.0 is about harnessing the potential of the Internet

In a more collaborative and peer-to-peer manner

Users communicate and collaborate while at the same time contribute and participate

Is shaping the way you work and interact with information on the web

Mindset change towards collaborative participation

Shifts the focus to the user of the information

User can search, choose, consume and modify the relevant content

Web 2.0 refers to the adoption of open technologies and architectural frameworks to facilitate participative computing

Principles of Web 2.0

Web 2.0 principles

Social Networks

Social Network A social network is structure made of nodes (representing people or organizations) that are connected together by one or more interdependencies (representing values, ideas, friendship, financial exchange, or trade) Represented as a social graph–based structure often very complex A web of trust exists in every social network nodes represent members of the web and edges represent the amount of trust among pairs of acquaintances Rapid emergence and acceptance of online social networks Computer Mediated Social Spaces (LinkedIn, Orkut, Facebook, SecondLife, Myspace) Peer to Peer Networks (Bit Torrent, Napster, KaZaA, Fasttrack, Freenet) Agent based systems (Cite-U-Like) Online transactions (Amazon, eBay)

A social network is structure made of nodes (representing people or organizations)

that are connected together by one or more interdependencies (representing values, ideas, friendship, financial exchange, or trade)

Represented as a social graph–based structure often very complex

A web of trust exists in every social network

nodes represent members of the web and edges represent the amount of trust among pairs of acquaintances

Rapid emergence and acceptance of online social networks

Computer Mediated Social Spaces (LinkedIn, Orkut, Facebook, SecondLife, Myspace)

Peer to Peer Networks (Bit Torrent, Napster, KaZaA, Fasttrack, Freenet)

Agent based systems (Cite-U-Like)

Online transactions (Amazon, eBay)

Sample Social Network

Social Network Analysis (SNA) and Web 2.0

Multitude of networks University networks Professional Networks Research Networks Product -based Networks State-wise Networks Language Networks Gaming Networks Student Networks Supplier/Buyer Networks Lifestyle Networks Entrepreneurship networks Software developer networks Family Networks Political Networks

Dimensions of Social Network formation Dimensions Scenarios 1 Space Physical, Virtual 2 Time Persistent, Campaign based 3 Theme Healthcare, Home, Gaming 4 Product/Commerce Wii, iPhone 5 Demographics State, Income, Race, Language 6 Life Cycle Teens, Adults, Middle Aged, Elderly 7 Customer Profile Single Parent, Single Professional, Separated professional, Retired Professional 9 Software/Tool based PC configurator, Mashups, Widgets 10 Enterprise Small Businesses, Mom & Pop stores 11 Entities Universities, Governments, Research Labs

Physical, Virtual

Persistent, Campaign based

Healthcare, Home, Gaming

Wii, iPhone

State, Income, Race, Language

Teens, Adults, Middle Aged, Elderly

Single Parent, Single Professional, Separated professional, Retired Professional

PC configurator, Mashups, Widgets

Small Businesses, Mom & Pop stores

Universities, Governments, Research Labs

Social Network Analysis

Social/Organizational Network Analysis Social Network Analysis (SNA) relates to mapping, understanding, analyzing and measuring interactions across a network of people Social networks, both formal as well as informal can foster knowledge sharing among participants This has interesting implications on enterprises wanting to leverage social networks to draw insights and inferences on user preferences as well as user participation in networks Using SNA, analysts can explore questions related to social networks such as Who are the members to watch? What are they saying? Where do they interact? Strength of interactions? Emergence of sub-groups? ----------

Social Network Analysis (SNA) relates to mapping, understanding, analyzing and measuring interactions across a network of people

Social networks, both formal as well as informal can foster knowledge sharing among participants

This has interesting implications on enterprises wanting to leverage social networks to draw insights and inferences on user preferences as well as user participation in networks

Using SNA, analysts can explore questions related to social networks such as

Who are the members to watch?

What are they saying?

Where do they interact?

Strength of interactions?

Emergence of sub-groups?

----------

Social/Organizational Network Analysis Social Network Analysis (SNA) is the mapping and measuring of relationships and flows between people (Borgatti et al 2002) Organizational Network Analysis (ONA) applies SNA to interactions in an organizational setting Focus on the persons involved i.e., the WHO question

Social Network Analysis (SNA) is the mapping and measuring of relationships and flows between people (Borgatti et al 2002)

Organizational Network Analysis (ONA) applies SNA to interactions in an organizational setting

Focus on the persons involved

i.e., the WHO question

SNA and Web 2.0

Key Question How do you derive value from Web 2.0 assets? Direct Better Customer/Consumer Experience Leading to Increased Customer Base Increased Sales Less Direct DATA from Web 2.0 assets as an ASSET Derived Better understanding of the customer Learning from the customer Customer driven innovation Examples: E-bay, Amazon

How do you derive value from Web 2.0 assets?

Direct

Better Customer/Consumer Experience

Leading to

Increased Customer Base

Increased Sales

Less Direct

DATA from Web 2.0 assets as an ASSET

Derived

Better understanding of the customer

Learning from the customer

Customer driven innovation

Examples: E-bay, Amazon

SNA and Web 2.0 Peer-to peer Peer-to peer network wherein collaboration and sharing are important activities Self managed collaboration as opposed to a central node-managed collaboration Wikis, blogs, video sharing etc. Collective Intelligence Lays emphasis on the large scale distributed Intelligence of the participants in the network over central Intelligence User created, modified, updated content User tagging, reviews etc.

Peer-to peer

Peer-to peer network wherein collaboration and sharing are important activities

Self managed collaboration as opposed to a central node-managed collaboration

Wikis, blogs, video sharing etc.

Collective Intelligence

Lays emphasis on the large scale distributed Intelligence of the participants in the network over central Intelligence

User created, modified, updated content

User tagging, reviews etc.

Amazon Recommendations Keeps track of browsing history, past purchases, your ratings as well as purchase by other users Include four types of ‘personalized’ recommendations Social recommendation (What Do Customers Ultimately Buy After Viewing This Item?) Item recommendation (New for You) Package recommendation (Frequently Bought Together) ‘ Others like you’ recommendation (Customers who bought …. also bought) Extensive customer reviews which include 1- 5 star ratings Favorable vs. Critical reviews Detailed review comments Your rating of the review comments (Help other customers find the most helpful reviews ) Comments on the review themselves

Keeps track of browsing history, past purchases, your ratings as well as purchase by other users

Include four types of ‘personalized’ recommendations

Social recommendation (What Do Customers Ultimately Buy After Viewing This Item?)

Item recommendation (New for You)

Package recommendation (Frequently Bought Together)

‘ Others like you’ recommendation (Customers who bought …. also bought)

Extensive customer reviews which include

1- 5 star ratings

Favorable vs. Critical reviews

Detailed review comments

Your rating of the review comments (Help other customers find the most helpful reviews )

Comments on the review themselves

Why invest in SNA

Why invest in SNA User/customer generated information could provide key insights which will aid decision making Insights into new products/services Informal listening board Influence customer decision making Social computing becoming popular Increasing role of communities

User/customer generated information could provide key insights which will aid decision making

Insights into new products/services

Informal listening board

Influence customer decision making

Social computing becoming popular

Increasing role of communities

Analysing Data

What is the data required? Online Individual Identity Assumptions Real identity may be unavailable Contact channel is available Multiple personalities/avatars possible Peer Evaluations Rating or “Respect” measures Message Data Sender Recipient (individual, group or online location) Content is text (for now…) Message threads more valuable Ability to relate one message to another Chronology of messages Online conversations Captured as log files Defined User Roles Enable online community to create user roles Map identity to user roles Uniform Time Stamps Chronology of all actions in the community

Online Individual Identity

Assumptions

Real identity may be unavailable

Contact channel is available

Multiple personalities/avatars possible

Peer Evaluations

Rating or “Respect” measures

Message Data

Sender

Recipient (individual, group or online location)

Content is text (for now…)

Message threads more valuable

Ability to relate one message to another

Chronology of messages

Online conversations

Captured as log files

Defined User Roles

Enable online community to create user roles

Map identity to user roles

Uniform Time Stamps

Chronology of all actions in the community

Why focus on the individual? Analyze past history of inputs Internal measure(s) of quality Community perspective(s) of quality Watch more closely their future inputs Presuming that Highly respected or individuals with high quality levels will provide higher quality inputs or insights in future Interact directly with those individuals Make them part of the “internal” team Understand interactions between individuals in the network

Analyze past history of inputs

Internal measure(s) of quality

Community perspective(s) of quality

Watch more closely their future inputs

Presuming that

Highly respected or individuals with high quality levels will provide higher quality inputs or insights in future

Interact directly with those individuals

Make them part of the “internal” team

Understand interactions between individuals in the network

How to go about understanding the data? Unit of analysis “ Message” Content sent from an individual sender to a recipient (individual or group) Message threads Identify concepts Categorizing messages Relate concepts and individuals Identify individuals related to concepts User Role User Status Links between individuals Sub-groups Links between concepts Locations on the network

Unit of analysis

“ Message”

Content sent from an individual sender to a recipient (individual or group)

Message threads

Identify concepts

Categorizing messages

Relate concepts and individuals

Identify individuals related to concepts

User Role

User Status

Links between individuals

Sub-groups

Links between concepts

Locations on the network

How to go about understanding the data? Contd… Link concept to source of the concept Determine reliability of Concept Source of the concept Through peer evaluation Discover issues of interest to the community As opposed to asking what we think is interesting Dynamic Analysis What has changed since the last time we looked?

Link concept to source of the concept

Determine reliability of

Concept

Source of the concept

Through peer evaluation

Discover issues of interest to the community

As opposed to asking what we think is interesting

Dynamic Analysis

What has changed since the last time we looked?

Tools and Products: Diagramming and Analysis Online Tools/Products BuddyGraph/Social Network Fragments (Experimental tool) Visible Path (Email) Metasight KMS (Email) ActiveNet/Illumio (Email + Documents) ContentExchange (Classification of user generated content) Traditional SNA Tools UCINet 6 MOST + SNA Pajek (Diagramming tool) Others CustomerConversation ZoomInfo

Online Tools/Products

BuddyGraph/Social Network Fragments (Experimental tool)

Visible Path (Email)

Metasight KMS (Email)

ActiveNet/Illumio (Email + Documents)

ContentExchange (Classification of user generated content)

Traditional SNA Tools

UCINet 6

MOST + SNA

Pajek (Diagramming tool)

Others

CustomerConversation

ZoomInfo

Other Techniques Collaborative Filtering Recommendation Engines Text mining Identify concepts and key words Web usage mining Usage patterns Identify what an individual is reading Process Mining Identify what sequence of activities take place

Collaborative Filtering

Recommendation Engines

Text mining

Identify concepts and key words

Web usage mining

Usage patterns

Identify what an individual is reading

Process Mining

Identify what sequence of activities take place

Interpreting the results and acting on it

Effective Use of the Network 4 dimensions for effective use of a network (Cross, Parker and Borgatti, 2002) Knowledge Knowing what someone knows Access Gaining timely access to that person Engagement Creating viable knowledge through cognitive engagement Safety Learning from a safe relationship

4 dimensions for effective use of a network (Cross, Parker and Borgatti, 2002)

Knowledge

Knowing what someone knows

Access

Gaining timely access to that person

Engagement

Creating viable knowledge through cognitive engagement

Safety

Learning from a safe relationship

Application Areas Customer Facing (External) “ Customer Intelligent Enterprise” Employee Facing (Internal) Break down internal silos Increase points of contact Hybrid (Customers and Employees) Facilitate interaction Direct connection to customers with insight and ideas

Customer Facing (External)

“ Customer Intelligent Enterprise”

Employee Facing (Internal)

Break down internal silos

Increase points of contact

Hybrid (Customers and Employees)

Facilitate interaction

Direct connection to customers with insight and ideas

Processes and Avenues Create/provide online venues for interaction Identify key network members Proactive contact with key members Facilitate interaction Connect key members to internal units Seed conversations (?) Facilitate listening/learning Feedback vs. listening

Create/provide online venues for interaction

Identify key network members

Proactive contact with key members

Facilitate interaction

Connect key members to internal units

Seed conversations (?)

Facilitate listening/learning

Feedback vs. listening

Issues to consider

What about... Data Sources Ownership Access Boundaries Of the firm Of the network Privacy and Other Legal Constraints Global network Local restrictions Processing Data Pre-processing Bias Formatting and storing Data Questions: When do I know I have something interesting? When do I know that something is no longer interesting?

Data Sources

Ownership

Access

Boundaries

Of the firm

Of the network

Privacy and Other Legal Constraints

Global network

Local restrictions

Processing Data

Pre-processing Bias

Formatting and storing Data

Questions:

When do I know I have something interesting?

When do I know that something is no longer interesting?

Conclusion

Conclusion Web 2.0 environments Rich source of data Huge potential to tap the insights of the consumer base Organizational Network Analysis Focus on the Individual/Community Identify likely sources of interesting data Watch for what they say in future Application Areas: Listening to Consumers Employees

Web 2.0 environments

Rich source of data

Huge potential to tap the insights of the consumer base

Organizational Network Analysis

Focus on the Individual/Community

Identify likely sources of interesting data

Watch for what they say in future

Application Areas: Listening to

Consumers

Employees

Thank You [email_address]

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