Going deep: Graph convolutional ladder-shape networks

Ruiqi Hu, Shirui Pan, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric Laplacian smoothing operation to aggregate the feature information of one node with that of its neighbors. While they have achieved great success in semi-supervised node classification on graphs, current approaches suffer from the over-smoothing problem when the depth of the neural networks increases, which always leads to a noticeable degradation of performance. To solve this problem, we present graph convolutional ladder-shape networks (GCLN), a novel graph neural network architecture that transmits messages from shallow layers to deeper layers to overcome the over-smoothing problem and dramatically extend the scale of the neural networks with improved performance. We have validated the effectiveness of proposed GCLN at a node-wise level with a semi-supervised task (node classification) and an unsupervised task (node clustering), and at a graph-wise level with graph classification by applying a differentiable pooling operation. The proposed GCLN outperforms original GCNs, deep GCNs and other state-of-the-art GCN-based models for all three tasks, which were designed from various perspectives on six real-world benchmark data sets.
Original languageEnglish
Title of host publicationProceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence
EditorsVincent Conitzer, Fei Sha
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages8
ISBN (Electronic)9781577358350
Publication statusPublished - 2020
EventAAAI Conference on Artificial Intelligence 2020 - New York, United States of America
Duration: 7 Feb 202012 Feb 2020
Conference number: 34th
https://aaai.org/Conferences/AAAI-20/ (Website)

Publication series

NameAAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


ConferenceAAAI Conference on Artificial Intelligence 2020
Abbreviated titleAAAI 2020
Country/TerritoryUnited States of America
CityNew York
OtherThe Thirty-Fourth AAAI Conference on Artificial Intelligence was held on February 7–12, 2020 in New York, New York, USA. The surge in public interest in AI technologies, which we have witnessed over the past few years, continued to accelerate in 2019–2020, with the societal and economic impact of AI becoming a central point of public and government discussion worldwide. AAAI-20 saw submissions and attendance numbers that were records in the history of the AAAI series of conferences and continued its tradition of attracting top-quality papers from all areas of AI. We were excited to see increases in submissions across almost all areas.

The AAAI-20 program consisted of a core technical program of original research presentations, including a special track on AI for social impact and a sister conference track. It additionally featured a broad range of tutorials, workshops, invited talks, panels, student abstracts, a debate, and presentations by senior members. The program was rounded out by technical demonstrations, exhibits, an AI job fair, the AI in Practice program, a student outreach program, and a game night. The conference also continued its tradition of colocating with the long-running IAAI conference and the EAAI symposium, as well as the newer conference on AI, Ethics, and Society.
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