Independence Promoted Graph Disentangled Networks

Liu, Yanbei; Wang, Xiao; Wu, Shu; Xiao, Zhitao

doi:10.1609/aaai.v34i04.5929

Cited by 59 publications

(29 citation statements)

References 10 publications

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“…To evaluate the performance of ADGCN, we choose to compare with several baselines, especially those that have achieved the state-of-the-art results recently. In addition to GCN [12] and GAT [33], which have been demonstrated excellent performance on graph-based tasks, we also compared the most related state-of-the-art works in disentangled graph learning such as DisenGCN [20] and IPGDN [17].…”

Section: Comparison Methodsmentioning

confidence: 99%

“…Specifically, the number K of the intermediate layers is set to gradually decrease to learn the hierarchical disentangled representation, and we use skip-connection to preserve the representation of different levels. As for the output dimension of first layer, it is set to K (1) • ∆d = 128, where K (1) ∈ [36] 59.0 59.6 71.1 LP [39] 68.0 45.3 63.0 DeepWalk [26] 67.2 43.2 65.3 ICA [6] 75.1 69.1 73.9 Planetoid [37] 75.7 64.7 77.2 ChebNet [5] 81.2 69.8 74.4 GCN [12] 81.5 70.3 79.0 MoNet [23] 81.7 -78.8 GAT [33] 83.0 72.5 79.0 DisenGCN [20] 83.7 73.4 80.5 IPGDN [17] 84 . As hyper-parameters, both K (1) and ∆K are searched through hyperopt [2].…”

Section: Implementation Detailsmentioning

confidence: 99%

“…For the node clustering task, we use the K-means [18] to cluster the learned representations as in [17]. The setting of the three datasets is the same as in semi-supervised node classification task.…”

Section: Clustering Analysismentioning

confidence: 99%

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Adversarial Graph Disentanglement

Zheng¹

2021

Preprint

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A real-world graph has a complex topology structure, which is often formed by the interaction of different latent factors. Disentanglement of these latent factors can effectively improve the robustness and interpretability of node representation of graph. However, most existing methods lack consideration of the intrinsic differences in links caused by factor entanglement. In this paper, we propose an Adversarial Disentangled Graph Convolutional Network (ADGCN) for disentangled graph representation learning. Specifically, a dynamic multi-component convolutional layer is designed to achieve micro-disentanglement by inferring latent components that caused links between nodes. On the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer that improves the separability between component distributions, thus to restrict interdependence among components. Additionally, to learn collaboratively a better disentangled representation and topological structure, a diversitypreserving node sampling based progressive refinement of graph structure is proposed. The experimental results on various real-world graph data verify that our ADGCN obtains remarkably more favorable performance over currently available alternatives.

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Section: Comparison Methodsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

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Adversarial Graph Disentanglement

Zheng¹

2021

Preprint

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“…Since different types of connections are generally treated as plain edges, GCNs individually represent each type of connection and aggregate them, which leads to redundant representations. Independence Promoted Graph Disentangled Network (IPGDN) [76] distinguishes the neighborhood into different parts and automatically discovers the nuances of graph's independent latent features, so that reduces the difficulty in detecting communities. IPGDN is supported by Hilbert-Schmidt Independence Criterion (HSIC) regularization [77] in neighborhood routings.…”

Section: B Gcn-based Community Detectionmentioning

confidence: 99%

A Comprehensive Survey on Community Detection With Deep Learning

Xue

Liu

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

170

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A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners. This survey devises and proposes a new taxonomy covering different categories of the state-of-theart methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

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“…Most recent works are based on the autoencoder architecture, where the latent features generated by the encoder are constrained to be independent in each dimension. The works of DisenGCN (Ma et al 2019) and IPGDN (Liu et al 2020), as pioneering attempts, achieve node-level disentanglement through neighbor routines that divide the neighbors of a node into several mutually exclusive parts. FactorGCN (Yang et al 2020), on the other hand, performs relation disentanglement by taking into account global topological semantics.…”

Section: Related Workmentioning

confidence: 99%

GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction

Wu¹,

Lin²,

Gao³

et al. 2021

Preprint

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Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node samples for different classes are balanced, while for many real-world graphs, there exists the problem of class imbalance, i.e., some classes may have much fewer samples than others. In this case, directly training a GNN classifier with raw data would under-represent samples from those minority classes and result in sub-optimal performance. This paper presents GraphMixup, a novel mixup-based framework for improving class-imbalanced node classification on graphs. However, directly performing mixup in the input space or embedding space may produce out-of-domain samples due to the extreme sparsity of minority classes; hence we construct semantic relation spaces that allows the Feature Mixup to be performed at the semantic level. Moreover, we apply two context-based self-supervised techniques to capture both local and global information in the graph structure and then propose Edge Mixup specifically for graph data. Finally, we develop a Reinforcement Mixup mechanism to adaptively determine how many samples are to be generated by mixup for those minority classes. Extensive experiments on three realworld datasets show that GraphMixup yields truly encouraging results for class-imbalanced node classification tasks.

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Independence Promoted Graph Disentangled Networks

Cited by 59 publications

References 10 publications

Adversarial Graph Disentanglement

Adversarial Graph Disentanglement

A Comprehensive Survey on Community Detection With Deep Learning

GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction

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