In Big Data era, Respondent-Driven Sampling (RDS) is more often applied network sampling with general population. Such optimization offers a possible solution for problems in traditional sampling investigation, including the difficulties to obtains usable sampling frames, respondents or the responds themselves. Moreover, it also enables network survey to be probabilistic and obtain overall parameter estimation within a certain error range. However, homogeneity in statistical research always deviates RDS estimating result (when recommending a companion for the research, the respondent is more likely to introduce someone with whom he/she shares similar qualities). In order to offer a practical solution, this paper assumes that population obeys the Degree-Corrected Stochastic Block Models (DCSBM). We post-stratify the sample based on transition probability and propose an inverse probability weighted PS-IPW estimator. By simulation analysis, we compare the relative efficiency between different network population with varied homogeneity. By empirical study, we sort out stratifies variables of our sample based on the characteristics of spectral of block matrix, which further verifies the usability of RDS sampling and the efficiency of PS-IPW estimator.