Deep Loopy Neural Network Model For Graph Structured Data Representation Learning

# Deep Loopy Neural Network Model for Graph Structured Data Representation Learning

**arXiv ID:** 1805.07504
**Authors:** Jiawei Zhang
**Published:** 2018-05-19T03:33:20Z
**Abstract:**
Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data.

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## References

- [arXiv:1805.07504](http://arxiv.org/abs/1805.07504v2)