Neural Network for Graphs: A Contextual Constructive Approach

计算机科学 建设性的 理论计算机科学 人工神经网络 人工智能 树遍历 概括性 过程(计算) 机器学习 算法 心理学 心理治疗师 操作系统
作者
Alessio Micheli
出处
期刊:IEEE Transactions on Neural Networks [Institute of Electrical and Electronics Engineers]
卷期号:20 (3): 498-511 被引量:492
标识
DOI:10.1109/tnn.2008.2010350
摘要

This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.
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