Production and Network Formation Games with Content Heterogeneity by Yu Zhang, Jaeok Park, and Mihaela van der Schaar.
Online social networks (e.g. Facebook, Twitter, Youtube) provide a popular, cost-effective and scalable framework for sharing user-generated contents. This paper addresses the intrinsic incentive problems residing in social networks using a game-theoretic model where individual users selfishly trade off the costs of forming links (i.e. whom they interact with) and producing contents personally against the potential rewards from doing so. Departing from the assumption that contents produced by difference users is perfectly substitutable, we explicitly consider heterogeneity in user-generated contents and study how it influences users’ behavior and the structure of social networks. Given content heterogeneity, we rigorously prove that when the population of a social network is sufficiently large, every (strict) non-cooperative equilibrium should consist of either a symmetric network topology where each user produces the same amount of content and has the same degree, or a two-level hierarchical topology with all users belonging to either of the two types: influencers who produce large amounts of contents and subscribers who produce small amounts of contents and get most of their contents from influencers. Meanwhile, the law of the few disappears in such networks. Moreover, we prove that the social optimum is always achieved by networks with symmetric topologies, where the sum of users’ utilities is maximized. To provide users with incentives for producing and mutually sharing the socially optimal amount of contents, a pricing scheme is proposed, with which we show that the social optimum can be achieved as a non-cooperative equilibrium with the pricing of content acquisition and link formation.
The “content heterogeneity” caught my eye but after reading the abstract, this appears relevant to topic maps for another reason.
One of the projects I hear discussed from time to time is a “public” topic map that encourages users to interact in a social context and to add content to the topic map. Group dynamics and the study of the same seem directly relevant to such “public” topic maps.
Interesting paper but I am not altogether sure about the “social optimum” as outlined in the paper. Not that I find it objectionable, but more that “social optimums” are a matter of social practice than engineering.