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dc.contributor.authorCampbell, Trevor David
dc.contributor.authorBroderick, Tamara A
dc.date.accessioned2020-12-10T21:33:13Z
dc.date.available2020-12-10T21:33:13Z
dc.date.issued2017-02
dc.identifier.issn1049-5258
dc.identifier.urihttps://hdl.handle.net/1721.1/128782
dc.description.abstractMany popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that these graphs are dense or empty with probability one, whereas many real-world graphs are sparse. We present an alternative notion of exchangeability for random graphs, which we call edge exchangeability, in which the distribution of a graph sequence is invariant to the order of the edges. We demonstrate that edge-exchangeable models, unlike models that are traditionally vertex exchangeable, can exhibit sparsity. To do so, we outline a general framework for graph generative models; by contrast to the pioneering work of Caron and Fox [12], models within our framework are stationary across steps of the graph sequence. In particular, our model grows the graph by instantiating more latent atoms of a single random measure as the dataset size increases, rather than adding new atoms to the measure.en_US
dc.language.isoen
dc.publisherMorgan Kaufmann Publishersen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceNeural Information Processing Systems (NIPS)en_US
dc.titleEdge-exchangeable graphs and sparsityen_US
dc.typeArticleen_US
dc.identifier.citationCai, Diana, Trevor Campbell and Tamara Broderick. “Edge-exchangeable graphs and sparsity.” Advances in Neural Information Processing Systems, 29 (February 2017) © 2017 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalAdvances in Neural Information Processing Systemsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-03T17:48:13Z
dspace.orderedauthorsCai, D; Campbell, T; Broderick, Ten_US
dspace.date.submission2020-12-03T17:48:16Z
mit.journal.volume29en_US
mit.licensePUBLISHER_POLICY


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