chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
473 changed files with 72932 additions and 0 deletions
@@ -0,0 +1,175 @@
+from __future__ import print_function
+
+import argparse
+import csv
+import time
+import warnings
+
+from datetime import datetime
+
+import easygraph as eg
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import torch
+
+from easygraph.datasets.citation_graph import CiteseerGraphDataset
+from easygraph.functions.community import greedy_modularity_communities
+from easygraph.functions.community import modularity
+from easygraph.functions.graph_embedding import *
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+
+
+warnings.filterwarnings("ignore")
+
+
+if __name__ == "__main__":
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    dataset = CiteseerGraphDataset(
+        force_reload=True
+    )  # Download CiteseerGraphDataset contained in EasyGraph
+    num_classes = dataset.num_classes
+    g = dataset[0]
+    labels = g.ndata["label"]
+    print(labels, labels.shape, len(g.nodes))
+
+    print("Graph embedding via DeepWalk...........")
+    deepwalk_emb, _ = deepwalk(g, dimensions=128, walk_length=80, num_walks=10)
+    # print(deepwalk_emb, len(deepwalk_emb))
+
+    dw_emb = []
+    for i in range(0, len(deepwalk_emb)):
+        dw_emb.append(list(deepwalk_emb[i]))
+    #   print(len(dw_emb))
+    dw_emb = np.array(dw_emb)
+    print(dw_emb)
+
+    tsne = TSNE(n_components=2, verbose=1, random_state=0)
+    z = tsne.fit_transform(dw_emb)
+    z_data = np.vstack((z.T, labels)).T
+    df_tsne = pd.DataFrame(z_data, columns=["Dim1", "Dim2", "class"])
+    df_tsne["class"] = df_tsne["class"].astype(int)
+    plt.figure(figsize=(8, 8))
+    sns.scatterplot(
+        data=df_tsne,
+        hue="class",
+        x="Dim1",
+        y="Dim2",
+        palette=["green", "orange", "brown", "red", "blue", "black"],
+    )
+    plt.savefig(
+        "figs/dw_citeseer.pdf", bbox_inches="tight"
+    )  # save embeddings if needed
+    plt.savefig("figs/dw_citeseer.png", bbox_inches="tight")
+    plt.show()
+
+    print("Graph embedding via Node2Vec..............")
+    node2vec_emb, _ = node2vec(
+        g, dimensions=128, walk_length=80, num_walks=10, p=4, q=0.25
+    )
+    # print(node2vec_emb, len(node2vec_emb))
+
+    n2v_emb = []
+    for i in range(0, len(node2vec_emb)):
+        n2v_emb.append(list(node2vec_emb[i]))
+    # print(len(n2v_emb))
+    n2v_emb = np.array(n2v_emb)
+    print(n2v_emb)
+
+    tsne = TSNE(n_components=2, verbose=1, random_state=0)
+    z = tsne.fit_transform(n2v_emb)
+    z_data = np.vstack((z.T, labels)).T
+    df_tsne = pd.DataFrame(z_data, columns=["Dim1", "Dim2", "class"])
+    df_tsne["class"] = df_tsne["class"].astype(int)
+    plt.figure(figsize=(8, 8))
+    sns.scatterplot(
+        data=df_tsne,
+        hue="class",
+        x="Dim1",
+        y="Dim2",
+        palette=["green", "orange", "brown", "red", "blue", "black"],
+    )
+
+    plt.savefig("figs/n2v_citeseer.pdf", bbox_inches="tight")
+    plt.savefig("figs/n2v_citeseer.png", bbox_inches="tight")
+    plt.show()
+
+    print("Graph embedding via LINE........")
+
+    model = LINE(
+        dimension=128,
+        walk_length=80,
+        walk_num=10,
+        negative=5,
+        batch_size=128,
+        init_alpha=0.025,
+        order=2,
+    )
+
+    model.train()
+    line_emb = model(g, return_dict=True)
+
+    l_emb = []
+    for i in range(0, len(line_emb)):
+        l_emb.append(list(line_emb[i]))
+    #   print(len(l_emb))
+    l_emb = np.array(l_emb)
+    print(l_emb)
+
+    tsne = TSNE(n_components=2, verbose=1, random_state=0)
+    z = tsne.fit_transform(l_emb)
+    z_data = np.vstack((z.T, labels)).T
+    df_tsne = pd.DataFrame(z_data, columns=["Dim1", "Dim2", "class"])
+    df_tsne["class"] = df_tsne["class"].astype(int)
+    plt.figure(figsize=(8, 8))
+    sns.scatterplot(
+        data=df_tsne,
+        hue="class",
+        x="Dim1",
+        y="Dim2",
+        palette=["green", "orange", "brown", "red", "blue", "black"],
+    )
+
+    plt.savefig("figs/line_citeseer.pdf", bbox_inches="tight")
+    plt.savefig("figs/line_citeseer.png", bbox_inches="tight")
+    plt.show()
+
+    print("Graph embedding via SDNE...........")
+    model = eg.SDNE(
+        g,
+        node_size=len(g.nodes),
+        nhid0=256,
+        nhid1=32,
+        dropout=0.025,
+        alpha=5e-4,
+        beta=10,
+    )
+    sdne_emb = model.train(model)
+
+    sd_emb = []
+    for i in range(0, len(sdne_emb)):
+        sd_emb.append(list(sdne_emb[i]))
+    #   print(len(sd_emb))
+    sd_emb = np.array(sd_emb)
+    print(sd_emb)
+
+    tsne = TSNE(n_components=2, verbose=1, random_state=0)
+    z = tsne.fit_transform(sd_emb)
+    z_data = np.vstack((z.T, labels)).T
+    df_tsne = pd.DataFrame(z_data, columns=["Dim1", "Dim2", "class"])
+    df_tsne["class"] = df_tsne["class"].astype(int)
+    plt.figure(figsize=(8, 8))
+    sns.scatterplot(
+        data=df_tsne,
+        hue="class",
+        x="Dim1",
+        y="Dim2",
+        palette=["green", "orange", "brown", "red", "blue", "black"],
+    )
+
+    plt.savefig("figs/sdne_citeseer2.pdf", bbox_inches="tight")
+    plt.savefig("figs/sdne_citeseer2.png", bbox_inches="tight")
+    plt.show()