p2p_tsne.py

from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler

tsne_data = merged_df.drop(["nodeId", "communityId"], axis=1).values.tolist()

scaler = StandardScaler()
scaler.fit(tsne_data)

tsne = TSNE(
    n_components=2, n_iter=500, random_state=42, perplexity=50, learning_rate=20
)
tsne_results = tsne.fit_transform(scaler.transform(tsne_data))

merged_df["tsne_x"] = [x[0] for x in list(tsne_results)]
merged_df["tsne_y"] = [x[1] for x in list(tsne_results)]

plt.figure(figsize=(18, 10))
sns.scatterplot(
    x="tsne_x",
    y="tsne_y",
    hue="communityId",
    palette=sns.color_palette("hls", 6),
    data=merged_df,
    legend="full",
    alpha=0.9,
)