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Mining Social Networks Using Heat Diffusion Processes for marketing candidates selection

Mining Social Networks Using Heat Diffusion Processes for marketing candidates selection. Hao Ma, Haixuan Yang, Michael R. Lyu and Irwin King CIKM, 2008. Reported by Wen-Chung Liao, 2009/10/6. Outlines. Motivation Objectives Diffusion models

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Mining Social Networks Using Heat Diffusion Processes for marketing candidates selection

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  1. Mining Social Networks Using Heat Diffusion Processes for marketing candidates selection Hao Ma, Haixuan Yang, Michael R. Lyu and Irwin King CIKM, 2008. Reported by Wen-Chung Liao, 2009/10/6

  2. Outlines • Motivation • Objectives • Diffusion models • Marketing candidates selection algorithms and their complexity • Empirical analysis • Conclusions • Comments

  3. Motivation • Due to the complexity of social networks, few models exist to interpret social network marketing realistically. • Studies of innovation diffusions, they were descriptive, rather than predictive • they are built at a very coarse level, typically with only a few global parameters • and are not useful for making actual predictions of the future behavior of the network.

  4. Objectives • Model social network marketing using Heat Diffusion Processes. • Presents three diffusion models, along with three algorithms for selecting the best individuals to receive marketing samples.

  5. HEAT DIFFUSION MODELS • The process of people influencing others is very similar to theheat diffusion phenomenon. • In a social network, the innovators and early adopters of a product or innovation act as heat sources.

  6. Diffusion on Undirected Social Networks G = (V,E) • G = (V,E) • V is the vertex set, and V = {v1, v2, . . . , vn}. • E is the set of all edges (vi, vj ). • fi(t) describes the heat at node vi at time t • fi(0) initial value. • f(t) denotes the vector consisting of fi(t). • M(i, j, Δt): amount of heat from node j to node i during a period Δt

  7. Diffusion on Directed Social Networks Diffusion on Directed Social Networks withPrior Knowledge

  8. Marketing candidates selection O(N(PM+N +N logN)) O(kN(PM +N +d)) O(N(PM +N +kd))

  9. EMPIRICAL ANALYSIS • Epinions • maintains a “trust” list which presents a network of trust relationships between users, • product categories, “Kids & Family” • 75,888 users, and 508,960 edges • the initial heat vector f(0), choose N/k • the thermal conductivity value α, set α= 1 • the adoption threshold θ, set θ = 0.6 • t = 0.10, t = 0.15 and t = 0.20, unit??? • Scenario: • 1 to 20 product samples (k =20) • the marketing candidates? • performance (measured by the value of coverage) ?

  10. EMPIRICAL ANALYSIS

  11. Conclusion • Propose a social network marketing framework which includes three diffusion models and three marketing candidates selection algorithms. • Model social network marketing as realistically as possible • Can defend against diffusion of negative information, • This framework is scalable.

  12. Comments • Advantage • Realistic & scalable. • Defend against diffusion of negative information • Shortage • Static social network. • My opinion:

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