Node2Vec algorithm results are not reliable

[cuneyttyler]

I am trying to test how node2vec works with a small graph here : https://ibb.co/Y7JWVTV

I calculated the cosine similarity between nodes 0,1 and 10. I'd expect similarity between 0 and 1 would be high and others would be low. However similarities are (-0.0070848754, 0.062368274, -0.13235472) for (0-1),(0-10),(1-10) pairs. Is it not reasonable to expect cosine similarities to be higher for close and interconnected nodes? If so, how can we measure the similarity and test the embeddings?

Thanks.

Here is my code:

graph_embedding_size = 100

def get_embedding(graph):
    edges_ = pd.DataFrame({
            'source': [e['source'] for e in graph['edges']],
            'target': [e['target'] for e in graph['edges']],
            'type': graph['edge_types']
        })

    G = StellarGraph(IndexedArray(index=graph['nodes']), edges_, edge_type_column="type")

    walk_length = 10
    rw = BiasedRandomWalk(G)
    walks = rw.run(
        nodes=G.nodes(),  # root nodes
        length=walk_length,  # maximum length of a random walk
        n=2,  # number of random walks per root node
        p=0.5,  # Defines (unormalised) probability, 1/p, of returning to source node
        q=2.0,  # Defines (unormalised) probability, 1/q, for moving away from source node
        weighted=False,  # for weighted random walks
        seed=42,  # random seed fixed for reproducibility
    )

    model = Word2Vec(
        walks,  vector_size=graph_embedding_size, window=5, min_count=0, sg=1, workers=1
    )

    return model.wv.vectors

graph_ex = {'nodes': [0,1,2,3,4,5,6,7,8,9,10], 
            'edges': [
                { 'source': 0,'target': 1},
                {'source': 0,'target': 2},
                {'source': 0,'target': 3},
                {'source': 1,'target': 2},
                {'source': 1,'target': 3},
                {'source': 2,'target': 3},
                {'source': 2,'target': 4},
                {'source': 4,'target': 5},
                {'source': 4,'target': 6},
                {'source': 5,'target': 6},
                {'source': 6,'target': 7},
                {'source': 5,'target': 8},
                {'source': 5,'target': 9},
                {'source': 9,'target': 10}
            ], 'edge_types': [1,1,1,1,1,1,1,1,1,1,1,1,1,1]}

embeddings_ex = get_embedding(graph_ex)

from numpy.linalg import norm

def cosine_sim(A,B):
    return np.dot(A,B)/(norm(A)*norm(B))

A = embeddings_ex[0]
B = embeddings_ex[1]
C = embeddings_ex[10]
cosine_sim(A,B),cosine_sim(A,C),cosine_sim(B,C)