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1. Weak Supervision Network Embedding for Constrained Graph Learning

   Guo, Ting ; Zhu, Xingquan ; Wang, Yang ; Chen, Fang ( May 2021 , Proc. of the Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science)

   Karlapalem, Kamal ; Cheng, Hong ; Ramakrishnan, Naren ; null ; null ; Reddy, P. Krishna ; Srivastava, Jaideep ; Chakraborty, Tanmoy (Ed.)

   Constrained learning, a weakly supervised learning task, aims to incorporate domain constraints to learn models without requiring labels for each instance. Because weak supervision knowledge is useful and easy to obtain, constrained learning outperforms unsupervised learning in performance and is preferable than supervised learning in terms of labeling costs. To date, constrained learning, especially constrained clustering, has been extensively studied, but was primarily focused on data in the Euclidean space. In this paper, we propose a weak supervision network embedding (WSNE) for constrained learning of graphs. Because no label is available for individual nodes, we propose a new loss function to quantify the constraint-based loss, and integrate this loss in a graph convolutional neural network (GCN) and variational graph auto-encoder (VGAE) combined framework to jointly model graph structures and node attributes. The joint optimization allows WSNE to learn embedding not only preserving network topology and content, but also satisfying the constraints. Experiments show that WSNE outperforms baselines for constrained graph learning tasks, including constrained graph clustering and constrained graph classification. 

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2. Multi-Class Imbalanced Graph Convolutional Network Learning

   https://doi.org/10.24963/ijcai.2020/398  

   Shi, Min ; Tang, Yufei ; Zhu, Xingquan ; Wilson, David ; Liu, Jianxun ( July 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI))

   null (Ed.)

   Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

    

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3. FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks

   https://doi.org/10.1109/IJCNN55064.2022.9892919  

   Santos, Francisco ; Ye, Junke ; Masrour, Farzan ; Tan, Pang-Ning ; Esfahanian, Abdol-Hossein ( July 2022 , Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN))

   Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph data due to their ability to learn a concise representation of the data by integrating the node attributes and link information in a principled fashion. However, despite their promise, there are several practical challenges that must be overcome to effectively use them for node classification problems. In particular, current approaches are vulnerable to different kinds of biases inherent in the graph data. First, if the class distribution is imbalanced, then the GNNs' loss function is biased towards classifying the majority class correctly rather than the minority class, which hurts the performance of the latter class. Second, due to homophily effect, the learned representation and subsequent downstream tasks may favor certain demographic groups over others when applied to social network data. To mitigate such biases, we propose a novel framework called Fairness-Aware Cost Sensitive Graph Convolutional Network (FACS-GCN) for classifying nodes in networks with skewed class distributions. Our approach combines a cost-sensitive exponential loss with an adversarial learning component to alleviate the ill-effects of both biases. The framework employs a stagewise additive modeling approach to ensure there is no significant loss in accuracy when imparting fairness into the GNN. Experimental results on 6 benchmark graph data demonstrate the effectiveness of FACS-GCN against comparable baseline methods in terms of promoting fairness while maintaining a high model accuracy on the majority of the datasets. 

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4. Multi-Class Imbalanced Graph Convolutional Network Learning

   Shi, Min ; Tang, Yufei ; Zhu, Xingquan ; Wilson, David ; Liu, Jianxun ( January 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20))

   null (Ed.)

   Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DRGCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization. 

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5. WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

   JINLI ZHANG, ZONGLI JIANG ( April 2020 , IEEE access)

   Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for improvement. Random walks based methods are currently popular methods to learn network embedding; however, they are random and limited by the length of sampled walks, and have difculty capturing network structural information. Some recent researches proposed using meta paths to express the sample relationship in HNs. Another popular graph learning model, the graph convolutional network (GCN) is known to be capable of better exploitation of network topology, but the current design of GCN is intended for homogenous networks. This paper proposes a novel combination of meta-graph and graph convolution, the meta-graph based graph convolutional networks (MGCN). To fully capture the complex long semantic information, MGCN utilizes different meta-graphs in HNs. As different meta-graphs express different semantic relationships, MGCN learns the weights of different meta-graphs to make up for the loss of semantics when applying GCN. In addition, we improve the current convolution design by adding node self-signicance. To validate our model in learning feature representation, we present comprehensive experiments on four real-world datasets and two representation tasks: classication and link prediction. WMGCN's representations can improve accuracy scores by up to around 10% in comparison to other popular representation learning models. What's more, WMGCN'feature learning outperforms other popular baselines. The experimental results clearly show our model is superior over other state-of-the-art representation learning algorithms. 

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