rohithteja

import networkx as nx
import numpy as np
import torch
from sklearn.preprocessing import StandardScaler

# load graph from networkx library
G = nx.karate_club_graph()

# retrieve the labels for each node
labels = np.asarray([G.nodes[i]['club'] != 'Mr. Hi' for i in G.nodes]).astype(np.int64)

rohithteja

import torch
import pandas as pd
from torch_geometric.data import InMemoryDataset, Data
from sklearn.model_selection import train_test_split
import torch_geometric.transforms as T

# custom dataset
class KarateDataset(InMemoryDataset):
    def __init__(self, transform=None):
        super(KarateDataset, self).__init__('.', transform, None, None)

rohithteja

import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv

# GCN model with 2 layers 
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(data.num_features, 16)
        self.conv2 = GCNConv(16, int(data.num_classes))

rohithteja

torch.manual_seed(42)

optimizer_name = "Adam"
lr = 1e-1
optimizer = getattr(torch.optim, optimizer_name)(model.parameters(), lr=lr)
epochs = 200

def train():
  model.train()
  optimizer.zero_grad()

rohithteja

from ge import DeepWalk

# load graph from networkx library
G = nx.karate_club_graph()

labels = np.asarray([G.nodes[i]['club'] != 'Mr. Hi' for i in G.nodes]).astype(np.int64)

# convert nodes from int to str format
keys = np.arange(0,34)
values = [str(i) for i in keys]

rohithteja

import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# retrieve the labels for each node
labels = np.asarray([G.nodes[i]['club'] != 'Mr. Hi' for i in G.nodes]).astype(np.int64)

# assigning colours to node labels
color_map = []
for i in labels:

rohithteja

import networkx as nx
import numpy as np
import torch

# load graph from networkx library
G = nx.karate_club_graph()

# retrieve the labels for each node
labels = np.asarray([G.nodes[i]['club'] != 'Mr. Hi' for i in G.nodes]).astype(np.int64)

rohithteja

import torch
import pandas as pd
from torch_geometric.data import InMemoryDataset, Data
from sklearn.model_selection import train_test_split
import torch_geometric.transforms as T

# custom dataset
class KarateDataset(InMemoryDataset):
    def __init__(self, transform=None):
        super(KarateDataset, self).__init__('.', transform, None, None)

rohithteja

import pandas as pd
import numpy as np
import dgl
import torch
from dgl.data import DGLDataset
from sklearn.model_selection import train_test_split

# prepare the embeddings corresponding to each node
nodes = pd.DataFrame(list(H.nodes()))
nodes.columns = ['nodes']

rohithteja

import torch
import networkx as nx
from spektral.data import Dataset
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from spektral.transforms import AdjToSpTensor, LayerPreprocess
from spektral.layers import GCNConv

# spektral custom dataset class
class KarateDataset(Dataset):