def METHOD_NAME(self):
    dataset = MAG240MDataset(self.data_dir)
    edge_path = f'{dataset.dir}/paper_to_paper_symmetric_pgl'
    t = time.perf_counter()
    if not osp.exists(edge_path):
        log.info('Converting adjacency matrix...')
        edge_index = dataset.edge_index('paper', 'cites', 'paper')
        edge_index = edge_index.T
        edges_new = np.zeros((edge_index.shape[0], 2))
        edges_new[:, 0] = edge_index[:, 1]
        edges_new[:, 1] = edge_index[:, 0]
        edge_index = np.vstack((edge_index, edges_new))
        edge_index = np.unique(edge_index, axis=0)
        graph = Graph(edge_index, sorted=True)
        graph.adj_dst_index
        graph.dump(edge_path)
        log.info(f'Done! [{time.perf_counter() - t:.2f}s]')
    np.random.seed(self.seed)
    self.train_idx = dataset.get_idx_split('train')
    np.random.shuffle(self.train_idx)
    self.val_idx = dataset.get_idx_split('valid')
    self.test_idx = dataset.get_idx_split('test')
    self.x = dataset.paper_feat
    self.y = dataset.all_paper_label
    self.graph = Graph.load(edge_path, mmap_mode='r+')
    log.info(f'Done! [{time.perf_counter() - t:.2f}s]')