4,196 research outputs found
Characterizing Efficient Referrals in Social Networks
Users of social networks often focus on specific areas of that network,
leading to the well-known "filter bubble" effect. Connecting people to a new
area of the network in a way that will cause them to become active in that area
could help alleviate this effect and improve social welfare.
Here we present preliminary analysis of network referrals, that is, attempts
by users to connect peers to other areas of the network. We classify these
referrals by their efficiency, i.e., the likelihood that a referral will result
in a user becoming active in the new area of the network. We show that by using
features describing past experience of the referring author and the content of
their messages we are able to predict whether referral will be effective,
reaching an AUC of 0.87 for those users most experienced in writing efficient
referrals. Our results represent a first step towards algorithmically
constructing efficient referrals with the goal of mitigating the "filter
bubble" effect pervasive in on line social networks.Comment: Accepted to the 2018 Web conference (WWW2018
Type Classes for Lightweight Substructural Types
Linear and substructural types are powerful tools, but adding them to
standard functional programming languages often means introducing extra
annotations and typing machinery. We propose a lightweight substructural type
system design that recasts the structural rules of weakening and contraction as
type classes; we demonstrate this design in a prototype language, Clamp.
Clamp supports polymorphic substructural types as well as an expressive
system of mutable references. At the same time, it adds little additional
overhead to a standard Damas-Hindley-Milner type system enriched with type
classes. We have established type safety for the core model and implemented a
type checker with type inference in Haskell.Comment: In Proceedings LINEARITY 2014, arXiv:1502.0441
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