تقرير
Graph Representation Ensemble Learning
| العنوان: | Graph Representation Ensemble Learning |
|---|---|
| المؤلفون: | Goyal, Palash, Huang, Di, Chhetri, Sujit Rokka, Canedo, Arquimedes, Shree, Jaya, Patterson, Evan |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Social and Information Networks, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Representation learning on graphs has been gaining attention due to its wide applicability in predicting missing links, and classifying and recommending nodes. Most embedding methods aim to preserve certain properties of the original graph in the low dimensional space. However, real world graphs have a combination of several properties which are difficult to characterize and capture by a single approach. In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently. We provide analysis of our framework and analyze -- theoretically and empirically -- the dependence between state-of-the-art embedding methods. We test our models on the node classification task on four real world graphs and show that proposed ensemble approaches can outperform the state-of-the-art methods by up to 8% on macro-F1. We further show that the approach is even more beneficial for underrepresented classes providing an improvement of up to 12%. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1909.02811 |
| رقم الأكسشن: | edsarx.1909.02811 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |