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+\documentclass{sig-alternate-2013}

+\pdfpagewidth=8.5in

+\pdfpageheight=11in

+\usepackage[utf8x]{inputenc}

+

+\usepackage{amsmath, amsfonts, amssymb, bbm}

+\usepackage{verbatim}

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+\newcommand{\inprod}[2]{#1 \cdot #2}

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+\DeclareMathOperator*{\argmax}{argmax}

+\DeclareMathOperator*{\argmin}{argmin}

+

+\newtheorem{theorem}{Theorem}

+\newtheorem{lemma}{Lemma}

+\newtheorem{corollary}{Corollary}

+\newtheorem{remark}{Remark}

+\newtheorem{proposition}{Proposition}

+\newtheorem{definition}{Definition}

+

+\permission{Permission to make digital or hard copies of part or all of this

+work for personal or classroom use is granted without fee provided that copies

+are not made or distributed for profit or commercial advantage, and that copies

+bear this notice and the full citation on the first page. Copyrights for

+third-party components of this work must be honored. For all other uses,

+contact the owner/author(s).  Copyright is held by the author/owner(s).}

+\conferenceinfo{WWW 2015 Companion,}{May 18--22, 2015, Florence, Italy.}

+\copyrightetc{ACM \the\acmcopyr}

+\crdata{978-1-4503-3473-0/15/05. \\

+http://dx.doi.org/10.1145/2740908.2744108}

+

+\clubpenalty=10000

+\widowpenalty = 10000

+

+\title{Scalable Methods for Adaptively Seeding A Social Network}

+

+\numberofauthors{2}

+\author{

+\alignauthor

+Thibaut Horel\\

+       \affaddr{Harvard University}\\

+       \email{thorel@seas.harvard.edu}

+\alignauthor

+Yaron Singer\\

+       \affaddr{Harvard University}\\

+       \email{yaron@seas.harvard.edu}

+}

+

+\begin{document}

+

+\maketitle

+

+\begin{abstract}

+In many applications of influence maximization, one is

+restricted to select influencers from a set of users who engaged with the topic

+being promoted, and due to the structure of social networks, these users often

+rank low in terms of their influence potential. An alternative approach one can

+consider is an adaptive method which selects users in a manner which targets

+their influential neighbors. The advantage of such an approach is that it

+leverages the friendship paradox in social networks: while users are often not

+influential, they often know someone who is.  

+

+Despite the various complexities in such optimization problems, we show that scalable adaptive seeding is achievable. In particular, we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized. To show the effectiveness of our methods we collected data from various verticals social network users follow. For each vertical, we collected data on the users who responded to a certain post as well as their neighbors, and applied our methods on this data. Our experiments show that adaptive seeding is  scalable, and importantly, that it obtains dramatic improvements over standard approaches of information dissemination.

+\end{abstract}

+

+\category{H.2.8}{Database Management}{Database Applications}[Data Mining]

+\category{F.2.2}{Analysis of Algorithms and Problem Complexity}{Nonnumerical Algorithms and Problems}

+\terms{Theory, Algorithms, Performance}

+\keywords{Influence Maximization; Two-stage Optimization}

+

+\section{Introduction}

+

+

+\bibliographystyle{abbrv}

+\bibliography{}

+

+%\balancecolumns

+\end{document}