Big Data: Social Network Analysis


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Introduction to the big data social network analysis

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  • The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
    The degree centrality considers nodes with the higher degrees (number of adjacent edges).
    The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
    The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
  • Big Data: Social Network Analysis

    1. 1. Social Network Michel Bruley WA - Marketing Director Extract from various presentations: B Wellman, K Toyama, A Sharma, Teradata Aster, … February 2012
    2. 2. Social Network 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
    3. 3. Society as a Graph People are represented as nodes Relationships are represented as edges: relationships may be acquaintanceship, friendship, co-authorship, etc. Allows analysis using tools of mathematical graph theory
    4. 4. Social Network Analysis Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities: Little Boxes Glocalization Networked Individualism
    5. 5. Connections Size – Number of nodes Density – Number of ties that are present / the amount of ties that could be present Out-degree – Sum of connections from an actor to others In-degree – Sum of connections to an actor
    6. 6. Distance Walk – A sequence of actors and relations that begins and ends with actors Geodesic distance – The number of relations in the shortest possible walk from one actor to another Maximum flow – The amount of different actors in the neighborhood of a source that lead to pathways to a target
    7. 7. Some Measures of Power & Prestige Degree – Sum of connections from or to an actor • Transitive weighted degreeAuthority, hub, pagerank Closeness centrality – Distance of one actor to all others in the network Betweenness centrality – Number that represents how frequently an actor is between other actors’ geodesic paths
    8. 8. Cliques and Social Roles Cliques – Sub-set of actors More closely tied to each other than to actors who are not part of the sub-set: – A lot of work on “trawling” for communities in the webgraph – Often, you first find the clique (or a densely connected subgraph) and then try to interpret what the clique is about Social roles – Defined by regularities in the patterns of relations among actors
    9. 9. Network Analysis Example
    10. 10. Centrality: strategic positions Degree centrality: Local attention Closeness centrality: Capacity to communicate Beetweenness centrality: reveal broker "A place for good ideas"
    11. 11. Social Network Analysis: what for? To control information flow To improve/stimulate communication To improve network resilience To trust Web applications of Social Networks examples: – Analyzing page importance (Page Rank, Authorities/Hubs) – Discovering Communities (Finding near-cliques) – Analyzing Trust (Propagating Trust, Using propagated trust to fight spam In Email or In Web page ranking)
    12. 12. Tangible Outcomes from SNA Sell More Better Knowledge Sharing Organisational Re-structures that work Preserving Expertise Building Better Communities More Innovation Competitive Intelligence
    13. 13. Ways to use SNA to Manage Churn Reduce Collateral Churn – – Reactive Identify subscribers whose loyalty is threatened by churn around them Reduce Influential Churn – – – Has churned Prevent collateral churn Preventive Identify subscribers who, should they churn, would take a few friends with them Need to go beyond individual value to network value ! • A subscriber with negative margin can have very significant network value and still be very valuable to keep Prevent influential churn
    14. 14. Cross-Sell and Technology Transfer 2 smartphone users around you  smartphone affinity x 5 !! Adopted Leverage Collateral Adoption – – Reactive Identify subscribers whose affinity for products is increased due to adoption around them & stimulate them Offer product Identify influencers for this adoption – Proactive – Identify subscribers who, should they adopt, would push a few friends to do the same Push for adoption
    15. 15. Acquisition – Member gets Member Campaign Topic Acquire New Members Description One of an Operator‘s major objectives is to keep (or even extend) the market position. As the main competitors are making ground by eg. attractive tariffs or through the acquisition of new retail partners, acquisition of new customers becomes a very important objective. This campaign format focuses on influencers in social communities, who are most likely to recommend a (off-net) friend to become a new subscriber of the Operator. The recommendation itself, as well as the subscription is incentivised for both, the subscriber and the recommending person.
    16. 16. Householding / Family identification a) Identify « same household » relationships – Construct probable household units • • – b) Identify onnet penetration Identify competitor position Identify probable decider(s) When multiple SIM cards purchased by same person, identify that other family members are using Sims – Age-related calling patterns Combination of a) and b)
    17. 17. Community Identification and Marketing Households / Families a)Seasonal workers b)SMEs c)Students d)Schoolchildren
    18. 18. Customer Lifestage analysis Analysis based on identifying critical life stage events using social network changes a) Going to University b) Moving c) Changing job d) Starting a relationship – Moving as a couple e) Imputing demographics – Age related patterns in the social network
    19. 19. Winback Campaign Topic Retention Description SNA offers an opportunity to detect potential churners earlier (possibly before they have completely ceased all on-net activity) and also identifies the individuals who are likely to have the best chance of persuading them to return. The aim is to use SNA to detect potential churners during the process of leaving and motivate them to stay with the Operator. Current Approach: New Approach Active Inactive Churn detected Churn detected
    20. 20. Competitor Insights a) Tracking dynamic changes in social networks based on competitor marketing activities • • • Reaction and impact of mass market campaigns Introduction of new products and tariffs Network evolution b) Improved accuracy in estimating operator market share • What does a competitor’s mass market campaigns do to the market? c) Segmenting competitors’ subscribers • Tracking segments based on selected SNA KPIs
    21. 21. Other business applications Facilitate Pre- to Post-Migration Identify Rotational Churners, switching between operators Identify Internal Churners Better customer lifecycle management by tracking customer network dynamics over his Lifecyle with the operator – Networks grow and change over time. This will influence how the operator interacts with the customer
    22. 22. Teradata Aster: See the Network Understand connections among users and organizations Challenges Examples • Large number of entities with rapidly growing amount of data for each • Connectivity changing constantly Aster Data Value •SQL-MapReduce® function for Graph Analysis eases and accelerates analysis •Ability to store and analyze massive volumes of data about users and connections • High loading throughput and incremental loading to bring new data into analysis • Link analysis: predicting connections (among people, products, etc.) that are likely to be of interest by looking at known connections • Influence analysis: identifying clusters and influencers in social networks
    23. 23. Teradata Aster References Social Network & Relationship Analysis Select Aster Data Customers in Digital Marketing Optimization Analysis of user behavior, intent, and actions across search, ad media and web properties, in order to drive increased ROI.