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dc.contributor.authorMalmi, Ericen_US
dc.contributor.authorGionis, Aristidesen_US
dc.contributor.authorTerzi, Evimariaen_US
dc.date.accessioned2018-07-20T13:21:18Z
dc.date.available2018-07-20T13:21:18Z
dc.date.issued2017
dc.identifier.citationEric Malmi, Aristides Gionis, Evimaria Terzi. 2017. "Active Network Alignment: A Matching-Based Approach." Proceeding CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1687-1696.
dc.identifier.urihttps://hdl.handle.net/2144/30012
dc.description.abstractNetwork alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications---from biological networks to social networks to ontologies---where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting. The majority of the existing active methods focus on absolute queries ("are nodes a and b the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set b1,...,bn is the most similar to node a?"). This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem. Our methods identify the most informative nodes to query by sampling the matchings of the bipartite graph associated to the network-alignment instance. We compare the proposed approaches to several commonly-used query strategies and perform experiments on both synthetic and real-world datasets. Our sampling-based strategies yield the highest overall performance, outperforming all the baseline methods by more than 15 percentage points in some cases. In terms of accuracy, TopMatchings and GibbsMatchings perform comparably. However, GibbsMatchings is significantly more scalable, but it also requires hyperparameter tuning for a temperature parameter.en_US
dc.description.urihttps://dl.acm.org/citation.cfm?doid=3132847.3132983
dc.description.urihttps://dl.acm.org/citation.cfm?doid=3132847.3132983
dc.relation.ispartofACM Conference on Information and Knowledge Management - CIKM
dc.subjectSocial and information networksen_US
dc.subjectPhysics and societyen_US
dc.subjectComputer scienceen_US
dc.titleActive network alignment: A matching-based approachen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1145/3132847.3132983
pubs.elements-sourcec-inst-1en_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US


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