Before revision:  For simple graphs, we demonstrate that learning algorithms defined onLE s tie with their performance on the original graphs, implying that no loss of information occurs in the expansion . For hypergraphs, we show that learning over the new representation leads to algorithms that beat all prior state-of-the-art hypergraph learning baselines .
After revision:  For simple graphs, we demonstrate that learning algorithms defined onLE We evaluate the proposed line expansion on five hypergraph datasets, the results show that our method beats SOTA baselines by a significant margin .