Name
Abstract | ||
|---|---|---|
We propose a principled approach for the problem of aligning multiple partially overlapping networks. The objective is to map 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 , introduces the idea of generalizing the 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. has the advantage of being able to leverage prior information on the number of entities, so that when this information is available, 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 . 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 . |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s10618-017-0505-2 | Data Min. Knowl. Discov. |
Keywords | Field | DocType |
Multiple network alignment,Facility location,Lagrangian relaxation,Genealogical trees,Social networks | Pairwise comparison,Data mining,Social network,Vertex (geometry),Generalization,Biological network,Computer science,Facility location problem,Ground truth,Artificial intelligence,Lagrangian relaxation,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 5 | 1384-5810 |
Citations | PageRank | References |
3 | 0.41 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Malmi | 1 | 49 | 8.81 |
| Sanjay Chawla | 2 | 1372 | 105.09 |
| Aristides Gionis | 3 | 6808 | 386.81 |