Active network alignment: a matching-based approach
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
- Boston University
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.
|Title of host publication||Proceedings of the 2017 ACM on Conference on Information and Knowledge Management|
|Publication status||Published - 2017|
|MoE publication type||A4 Article in a conference publication|
|Event||ACM International Conference on Information & Knowledge Management - Pan Pacific Singapore, Singapore, Singapore|
Duration: 6 Nov 2017 → 10 Nov 2017
Conference number: 26
|Conference||ACM International Conference on Information & Knowledge Management|
|Period||06/11/2017 → 10/11/2017|