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

Dr. Jai GaneshWeb 2.0 Research Lab

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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

Overview of Web 2.0

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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

Principles of Web 2.0

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Web 2.0 principles

Social Networks

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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)

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Sample Social Network

Social Network Analysis (SNA) and Web 2.0

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Multitude of networks

University networks

ProfessionalNetworks

ResearchNetworks

Product -based Networks

State-wise Networks

Language Networks

GamingNetworksStudent

Networks

Supplier/BuyerNetworks

Lifestyle NetworksEntrepreneurship

networks

Software developer networks

FamilyNetworks

PoliticalNetworks

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Dimensions of Social Network formation

Dimensions Scenarios1 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

Social Network Analysis

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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?

• ----------

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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

SNA and Web 2.0

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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

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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.

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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

Why invest in SNA

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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

Analysing Data

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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

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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

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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

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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?

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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

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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

Interpreting the results and acting on it

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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

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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

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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

Issues to consider

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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?

Conclusion

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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

Thank You

Jai_ganesh01@infosys.com