BSOGCN: Brain Storm Optimization Graph Convolutional Networks Based Heterogeneous Information Networks Embedding

Qu, Liang; Zhu, Huaisheng; Shi, Yuhui

doi:10.1109/cec48606.2020.9185532

Cited by 2 publications

(1 citation statement)

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“…Typical network embedding methods could be roughly divided into two categories, unsupervised network embedding methods [27] and semi-supervised network embedding methods [25]. The former obtains the node embeddings by preserving the network structure information.…”

Section: Introductionmentioning

confidence: 99%

ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks

Qu,

Zhu,

Zheng

et al. 2021

Preprint

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Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks. However, most existing GNNs have almost exclusively focused on the balanced networks, and would get unappealing performance on the imbalanced networks. To bridge this gap, in this paper, we present a generative adversarial graph network model, called ImGAGN to address the imbalanced classification problem on graphs. It introduces a novel generator for graph structure data, named GraphGenerator, which can simulate both the minority class nodes' attribute distribution and network topological structure distribution by generating a set of synthetic minority nodes such that the number of nodes in different classes can be balanced. Then a graph convolutional network (GCN) discriminator is trained to discriminate between real nodes and fake (i.e., generated) nodes, and also between minority nodes and majority nodes on the synthetic balanced network. To validate the effectiveness of the proposed method, extensive experiments are conducted on four real-world imbalanced network datasets. Experimental results demonstrate that the proposed method Im-GAGN outperforms state-of-the-art algorithms for semi-supervised imbalanced node classification task. CCS CONCEPTS• Computing methodologies → Neural networks.

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Section: Introductionmentioning

confidence: 99%