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

  • 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
  • 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
  • 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)
  • 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
  • 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/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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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?
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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 [email_address]