TY - JOUR
T1 - Lagrangian relaxations for multiple network alignment
AU - Malmi, Eric
AU - Chawla, Sanjay
AU - Gionis, Aristides
N1 - Publisher Copyright:
© 2017, The Author(s).
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
KW - Facility location
KW - Genealogical trees
KW - Lagrangian relaxation
KW - Multiple network alignment
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=85016493281&partnerID=8YFLogxK
U2 - 10.1007/s10618-017-0505-2
DO - 10.1007/s10618-017-0505-2
M3 - Article
AN - SCOPUS:85016493281
SN - 1384-5810
VL - 31
SP - 1331
EP - 1358
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 5
ER -