1. How to get a small degree of bias from a clear community structure needs more explanations. Theorem 1 and 2 prove that GCL conforms to a clearer community structure via intra-community concentration and inter-community scatter, but its relationship with degree bias is not intuitive enough. 2. There is some confusion in the theoretical analysis. Why is the supremum in Definition 1 \gamma(\frac{B}{\hat{d}_{\min}^k})^{\frac{1}{2}}? Based on this definition, how to prove that the proposed GRADE reduces this supremum? 3. There is a lack of significance test in Table 1. Despite the weaknesses mentioned above, I believe that this paper is worth publishing. They consider an important degree-bias problem in the graph domain, given that node degrees of real-world graphs often follow a long-tailed power-law distribution. And they show an exciting finding that GCL is more stable w.r.t. the degree bias, and give a preliminary explanation for the underlying mechanism. Although the improvement does not seem significant in Table 1, they may inspire more future research on this promising solution.
1. How to get a small degree of bias from a clear community structure needs more explanations. Theorem 1 and 2 prove that GCL conforms to a clearer community structure via intra-community concentration and inter-community scatter, but its relationship with degree bias is not intuitive enough.