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        <identifier>oai:drops-oai.dagstuhl.de:4906</identifier>
        <datestamp>2024-03-06T10:35:31Z</datestamp>
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          <dc:title>Welfare Maximization with Friends-of-Friends Network Externalities</dc:title>
          <dc:creator>Bhattacharya, Sayan</dc:creator>
          <dc:creator>Dvorák, Wolfgang</dc:creator>
          <dc:creator>Henzinger, Monika</dc:creator>
          <dc:creator>Starnberger, Martin</dc:creator>
          <dc:subject>network externalities</dc:subject>
          <dc:subject>welfare maximization</dc:subject>
          <dc:subject>approximation algorithms</dc:subject>
          <dc:description>Online social networks allow the collection of large amounts of data about the influence between users connected by a friendship-like relationship. When distributing items among agents forming a social network, this information allows us to exploit network externalities that each agent receives from his neighbors that get the same item. In this paper we consider Friends-of-Friends (2-hop) network externalities, i.e., externalities that not only depend on the neighbors that get the same item but also on neighbors of neighbors. For these externalities we study a setting where multiple different items are assigned to unit-demand agents. Specifically, we study the problem of welfare maximization under different types of externality functions. Let n be the number of agents and m be the number of items. Our contributions are the following: (1) We  show that welfare maximization is APX-hard; we show that even for step functions with 2-hop (and also with 1-hop) externalities it is NP-hard to approximate social welfare better than (1-1/e). (2) On the positive side we present  (i) an O(sqrt n)-approximation algorithm for general concave externality functions,&#13;
(ii) an O(\log m)-approximation algorithm for linear externality functions, and (iii) an (1-1/e)\frac{1}{6}-approximation algorithm for 2-hop step function externalities. We also improve the result from [6] for 1-hop step function externalities by giving  a (1-1/e)/2-approximation algorithm.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Sayan Bhattacharya and Wolfgang Dvorák and Monika Henzinger and Martin Starnberger</dc:contributor>
          <dc:date>2015</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 30, 32nd International Symposium on Theoretical Aspects of Computer Science (STACS 2015)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.STACS.2015.90</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-49066</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2015.90</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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