Graph neural networks (GNNs) have recently demonstrated significant success. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a low cost. However, most existing methods for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. This further adversely affects the classification performance. To tackle this issue, in this paper, we propose a novel reinforced class-balanced active learning framework for GNNs, namely, GraphCBAL. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GraphCBAL designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. We further upgrade GraphCBAL to GraphCBAL++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines. In particular, our methods can strike the balance between classification results and class balance. We provide our code and data at https://github.com/cici-chengcheng/GraphCBAL.

In recent years, the world economy is being transformed into a digital one. E-commerce has brought a lot of convenience people's life, which is followed by the exponential growth of online reviews of e-commerce. These comments are very valuable for e-commerce and users. This paper proposes an effective method of summarizing short comments information based on Chinese. This method uses a series of phrases composed by noun and adjective as the final presentation. Firstly, the noun and adjective phrases in each comment were extracted, then the LDA probability model was used for content clustering, and finally the content summary of each category was extracted according the frequency of occurrence. In this paper, the real comment data of Taobao is used for experiments, and the results show that the proposed method can effectively summarize valuable information of commodities.