The proposed matrix-based sampling approach can be extended to support other types of sampling algorithms by adapting the matrix operations to fit the specific requirements of each algorithm. For example, for a graph sampling algorithm that involves sampling based on node attributes or edge weights, the matrix-based approach can be modified to incorporate these additional factors into the sampling process. This may involve adjusting the probability distributions generated in the sampling step or customizing the row and column extraction operations to consider different features of the graph.
By representing the sampling algorithms in terms of matrix operations, it becomes easier to generalize the approach to accommodate various types of sampling strategies. Each algorithm can be implemented as a series of matrix manipulations, making it more scalable and adaptable to different graph structures and sampling methodologies. Additionally, by leveraging the efficiency of sparse matrix operations, the matrix-based approach can handle complex sampling algorithms with large graphs efficiently.