Geometric Pooling: maintaining more useful information

Autor:	Xu, Hao, Liu, Jia, Shen, Yang, Lou, Kenan, Bao, Yanxia, Zhang, Ruihua, Zhou, Shuyue, Zhao, Hongsen, Wang, Shuai
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain useful information and can contribute considerably to the final decision. To maintain more useful information, a novel pooling technology, called Geometric Pooling (GP), was proposed to contain the unique node features with negative values by measuring the similarity of all node features. We reveal the effectiveness of GP from the entropy reduction view. The experiments were conducted on TUdatasets to show the effectiveness of GP. The results showed that the proposed GP outperforms the SOTA graph pooling technologies by 1%\sim5% with fewer parameters. Comment: Accepted by IEEE ACCESS
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.12341 Zobrazit plný text záznamu View this record from Arxiv