Learning Graph Augmentations to Learn Graph Representations

Hassani, Kaveh; Khasahmadi, Amir Hosein

doi:10.48550/arxiv.2201.09830

Cited by 1 publication

(2 citation statements)

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“…GROC [28] proposes a rule-based method to modify the edges. LG2AR [29] proposes a data augmentation method based on the distribution of all nodes in the graph. Another group of methods proposes to augment data by sub-graph sampling.…”

Section: Graph Contrastive Learningmentioning

confidence: 99%

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Representing Spatial Data with Graph Contrastive Learning

et al. 2023

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Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines.

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Section: Graph Contrastive Learningmentioning

confidence: 99%

“…Therefore, we propose to augment data by considering the importance of each node. A natural idea to measure a node's importance is to calculate the centrality measure of the node [11,28,29]. However, most of the existing centrality measure methods focus on homogeneous graphs.…”

Section: Data Augmentationmentioning

confidence: 99%