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A comparative study on network alignment techniques
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DOI：10.1016/j.eswa.2019.112883.png)
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Abstract
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Network alignment is a method to align nodes that belong to the same entity from different networks. A well-known application of network alignment is to map user accounts from different social networks that belong to the same person. As network alignment has a wide range of applications from recommendation to link prediction, there are several proposed approaches to aligning nodes from different networks. These techniques, however, have been rarely compared and analyzed under the same setting, rendering a right choice for a particular set of networks very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of network alignment methods. Specifically, we integrate several state-of-the-art network alignment techniques in a comparable manner, and measure distinct characteristics of these techniques with various settings. We then provide in-depth analysis of the benchmark results, obtained by using both real data and synthetic data. We believe that the findings from the benchmark will serve as a practical guideline for potential applications. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Network alignment
Graph matching
Network embedding
Graph mining
Node representation learning
Low-rank matrix factorization
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