Graph Self-Contrast Representation Learning

Graph contrastive learning (GCL) has recently emerged as a promising approach for graph representation learning. Some existing methods adopt the 1-vs-K scheme to construct one positive and K negative samples for each graph, but it is difficult to set K. For those methods that do not use negative samples, it is often necessary to add additional strategies to avoid model collapse, which could only alleviate the problem to some extent. All these drawbacks will undoubtedly have an adverse impact on the generalizability and efficiency of the model. In this paper, to address these issues, we propose a novel graph self-contrast framework GraphSC, which only uses one positive and one negative sample, and chooses triplet loss as the objective. Specifically, self-contrast has two implications. First, GraphSC generates both positive and negative views of a graph sample from the graph itself via graph augmentation functions of various intensities, and use them for self-contrast. Second, GraphSC uses Hilbert-Schmidt Independence Criterion (HSIC) to factorize the representations into multiple factors and proposes a masked self-contrast mechanism to better separate positive and negative samples. Further, Since the triplet loss only optimizes the relative distance between the anchor and its positive/negative samples, it is difficult to ensure the absolute distance between the anchor and positive sample. Therefore, we explicitly reduced the absolute distance between the anchor and positive sample to accelerate convergence. Finally, we conduct extensive experiments to evaluate the performance of GraphSC against 19 other state-of-the-art methods in both unsupervised and transfer learning settings.Comment: ICDM 2023(Regular

Effectiveness of the River Chief System in China: A Study Based on Grassroots River Chief’s Behavior

The River Chief System is an administrative model of water environment governance currently adopted in China. Under this system, the chief CPC and government leaders at various levels serve as “river chiefs” and are responsible for organizing and directing the management and protection of the rivers and lakes within their remit. This paper tries to reveal the actual effectiveness of the River Chief System based on the behaviors of grassroots river chiefs (GRCs). First-hand data about GRCs is obtained through a questionnaire survey. Whether the water environment governance target is achieved and the water quality change of the river sections in the charge of GRCs is quantitatively assessed It has been found that, except for implementing “one policy for one river” and making river patrols, the behaviors of GRCs have no positive effect on river pollution prevention and control, implying the ineffectiveness of the River Chief System. The framework design of the River Chief System should be optimized, and a system with professionals to support GRCs in performing their duties should be established. Moreover, the tendency to use environmental regulation as a mandatory policy tool should be weakened. These measures are of great practical significance to the implementation of the green development concept and the furthering of the River Chief System overall