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

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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 …

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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  • 1. Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks Dr. Jai Ganesh Web 2.0 Research Lab
  • 2. 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
  • 3. Overview of Web 2.0
  • 4. 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
  • 5. Principles of Web 2.0
  • 6. Web 2.0 principles
  • 7. Social Networks
  • 8. 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)
  • 9. Sample Social Network
  • 10. Social Network Analysis (SNA) and Web 2.0
  • 11. 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
  • 12. 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
  • 13. Social Network Analysis
  • 14. 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?
        • ----------
  • 15. 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
  • 16. SNA and Web 2.0
  • 17. 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
  • 18. 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.
  • 19. 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
  • 20. Why invest in SNA
  • 21. 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
  • 22. Analysing Data
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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?
  • 27. 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
  • 28. 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
  • 29. Interpreting the results and acting on it
  • 30. 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
  • 31. 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
  • 32. 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
  • 33. Issues to consider
  • 34. 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?
  • 35. Conclusion
  • 36. 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
  • 37. Thank You [email_address]