network_visualzation_dz9_2

def xy_embeddings(_graph):
    graph = _graph.copy()
    ### BEGIN SOLUTION
    pbar = tqdm(total=4)

    model = LaplacianEigenmaps(dimensions=16)
    model.fit(graph)
    laplacian_emb = model.get_embedding()
    model = PCA(n_components=2)
    model.fit(laplacian_emb)
    laplacian_emb = model.transform(laplacian_emb)
    pbar.update(1)

    A = nx.to_numpy_array(graph)
    model = TruncatedSVD(n_components=16)
    model.fit(A)
    svd_emb = model.transform(A)
    model = PCA(n_components=2)
    model.fit(svd_emb)
    svd_emb = model.transform(svd_emb)
    pbar.update(1)

    model = DeepWalk(
        walk_number=10, walk_length=30, dimensions=16, window_size=10)
    model.fit(graph)
    deep_walk_emb = model.get_embedding()
    model = PCA(n_components=2)
    model.fit(deep_walk_emb)
    deep_walk_emb = model.transform(deep_walk_emb)
    pbar.update(1)

    model = MeanWalklets(
        walk_number=10, walk_length=30, dimensions=16, window_size=10)
    model.fit(graph)
    walklets_emb = model.get_embedding()
    model = PCA(n_components=2)
    model.fit(walklets_emb)
    walklets_emb = model.transform(walklets_emb)
    pbar.update(1)

    pbar.close()

    return laplacian_emb, svd_emb, deep_walk_emb, walklets_emb
    ### END SOLUTION