Informative Nodes Mining for Class-Imbalanced Representation Learning

Mengting Zhou, Zhiguo Gong

Abstract: Online social networks often enforce a restricted web interface to allow third part users to access their data objects in one-by-one manner along links. The heavy cost of collecting data from them makes it very difficult to train a graph neural networks (GNNs) algorithm by using valuable data resources from those social network services. In this article, we endeavor to harness the potential of collected data in order to cultivate robust node representations, all while taking into account the prevalent class-imbalanced scenario. To this end, we propose a framework based on the reinforcement learning technique that can implement training while sampling. We employee deep Q-learning network (DQN) as the basic reinforcement learning framework which maintains an experience replay buffer to store experiences in order to improve efficiency and generalization ability of the algorithm. The proposed framework can guide the training process according to the label distribution of the currently collected training data. To maximize the utility of the collected data, we introduce two ways to guide the agent to learn more robust node representations under the natural class-imbalanced scenario. Specifically, effective-number GNNs (ENG) try to employ a reward function with the technique of effective number and resampling GNNs (RSG) attempts to modify the state transition function motivated by resampling technique. Extensive experiments are conducted on several real-world imbalanced datasets and the proposed methods significantly outperform the state-of-the-art methods in node classification task, accomplishing higher performance while utilizing a reduced volume of data.

External IDs:dblp:journals/tcss/ZhouG25