Big graph classification frameworks based on Extreme Learning Machine
Big graph classification frameworks based on Extreme Learning Machine
复制标题
基于极限学习机的大图分类框架
DOI：
10.1016/j.neucom.2018.11.035
复制
发表时间：
2019
期刊：
影响因子：
6
通讯作者：
Wang Guoren
中科院分区：
文献类型：
--
作者：
Sun Yongjiao;Li Boyang;Yuan Ye;Bi Xin;Zhao Xiangguo;Wang Guoren
图数据分析是近年来研究的热点问题。图分类是图数据分析中最重要的问题之一，它是在训练数据集的基础上，利用模型来选择图的最可能的类标签。图学习在蛋白质组识别、化合物分类等领域有着广泛的应用，但随着大规模图数据的急剧增加，现有的许多图学习研究都面临着计算量大的问题。为了实现具有实时学习能力和良好可扩展性的大图分类，本文采用了有效的特征提取方法和ELM变体。具体而言，本文提出了三种基于ELM的大图分类框架：（1）基于压缩的频繁子图挖掘框架，以减小图的规模;（2）增量式框架，以处理动态图;（3）分布式ELM框架，以提供良好的可扩展性和易于在云平台上实现。大量的实验进行了聚类与大型现实世界的图形数据集。实验结果表明，我们的框架是有效的大图分类应用程序，并适合于动态网络。实验结果也验证了ELM及其变体在大规模图上具有良好的分类性能。
Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models based on the training dataset. It has wildly applications in protein group identification, chemical compounds classification and so on. Many existing research of graph learning suffer from high computation cost as large scale graph data are dramatically increased. In order to realize big graph classification with real-time learning ability and good scalability, efficient feature extraction approaches and ELM variants are utilized in this paper. To be specific, we present three frameworks of big graph classification based on ELMs: (1) a framework with a compression-based frequent subgraph mining method to reduce graph size; (2) an incremental framework to handle dynamic graphs; (3) a distributed framework with distributed ELMs to provide good scalability and easy implementation on cloud platforms. Extensive experiments are conducted on clusters with large real-world graph datasets. The experimental results demonstrate that our frameworks are efficient in big graph classification applications, and well suitable for dynamic networks. The results also validate that ELM and its variants have good classification performance on large-scale graphs.