Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1331–1358 | Cite as

Lagrangian relaxations for multiple network alignment

  • Eric Malmi
  • Sanjay Chawla
  • Aristides Gionis
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017


We propose a principled approach for the problem of aligning multiple partially overlapping networks. The objective is to map multiple graphs into a single graph while preserving vertex and edge similarities. The problem is inspired by the task of integrating partial views of a family tree (genealogical network) into one unified network, but it also has applications, for example, in social and biological networks. Our approach, called Flan, introduces the idea of generalizing the facility location problem by adding a non-linear term to capture edge similarities and to infer the underlying entity network. The problem is solved using an alternating optimization procedure with a Lagrangian relaxation. Flan has the advantage of being able to leverage prior information on the number of entities, so that when this information is available, Flan is shown to work robustly without the need to use any ground truth data for fine-tuning method parameters. Additionally, we present three multiple-network extensions to an existing state-of-the-art pairwise alignment method called Natalie. Extensive experiments on synthetic, as well as real-world datasets on social networks and genealogical networks, attest to the effectiveness of the proposed approaches which clearly outperform a popular multiple network alignment method called IsoRankN.


Multiple network alignment Facility location Lagrangian relaxation Genealogical trees Social networks 



The authors are grateful to Pekka Valta and the Genealogical Society of Finland for providing the family tree dataset, to Jukka Suomela for useful discussions on Flan, to Gunnar W. Klau for his advice on extending Natalie to multiple networks, and to the anonymous reviewers for their constructive comments. This work was supported by Academy of Finland Project “Nestor” (286211).


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.HIITAalto UniversityEspooFinland
  2. 2.Qatar Computing Research Institute, HBKUDohaQatar

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