On size-oriented long-tailed graph classification of graph neural networks

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In partic...

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Main Authors: LIU, Zemin, MAO, Qiheng, LIU, Chenghao, FANG, Yuan, SUN, Jianling
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7489
https://ink.library.smu.edu.sg/context/sis_research/article/8492/viewcontent/TheWebConf22_SOLT.pdf
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spelling sg-smu-ink.sis_research-84922022-11-03T06:28:58Z On size-oriented long-tailed graph classification of graph neural networks LIU, Zemin MAO, Qiheng LIU, Chenghao FANG, Yuan SUN, Jianling The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. In particular, SOLTGNN capitalizes on the co-occurrence substructures exploitation to extract the transferable patterns from head graphs. Furthermore, a novel relevance prediction function is proposed to memorize the pattern relevance derived from head graphs, in order to predict the complements for tail graphs to attain more comprehensive structures for enrichment. We conduct extensive experiments on five benchmark datasets, and demonstrate that our proposed model can outperform the state-of-the-art baselines. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7489 info:doi/10.1145/3485447.3512197 https://ink.library.smu.edu.sg/context/sis_research/article/8492/viewcontent/TheWebConf22_SOLT.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Size-oriented long-tailed distribution graph neural networks knowledge transfer Graphics and Human Computer Interfaces OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Size-oriented long-tailed distribution
graph neural networks
knowledge transfer
Graphics and Human Computer Interfaces
OS and Networks
spellingShingle Size-oriented long-tailed distribution
graph neural networks
knowledge transfer
Graphics and Human Computer Interfaces
OS and Networks
LIU, Zemin
MAO, Qiheng
LIU, Chenghao
FANG, Yuan
SUN, Jianling
On size-oriented long-tailed graph classification of graph neural networks
description The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. In particular, SOLTGNN capitalizes on the co-occurrence substructures exploitation to extract the transferable patterns from head graphs. Furthermore, a novel relevance prediction function is proposed to memorize the pattern relevance derived from head graphs, in order to predict the complements for tail graphs to attain more comprehensive structures for enrichment. We conduct extensive experiments on five benchmark datasets, and demonstrate that our proposed model can outperform the state-of-the-art baselines.
format text
author LIU, Zemin
MAO, Qiheng
LIU, Chenghao
FANG, Yuan
SUN, Jianling
author_facet LIU, Zemin
MAO, Qiheng
LIU, Chenghao
FANG, Yuan
SUN, Jianling
author_sort LIU, Zemin
title On size-oriented long-tailed graph classification of graph neural networks
title_short On size-oriented long-tailed graph classification of graph neural networks
title_full On size-oriented long-tailed graph classification of graph neural networks
title_fullStr On size-oriented long-tailed graph classification of graph neural networks
title_full_unstemmed On size-oriented long-tailed graph classification of graph neural networks
title_sort on size-oriented long-tailed graph classification of graph neural networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7489
https://ink.library.smu.edu.sg/context/sis_research/article/8492/viewcontent/TheWebConf22_SOLT.pdf
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