tomonori-masui

model = Net(dataset.num_features, 128, 64).to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01)
criterion = torch.nn.BCEWithLogitsLoss()
model = train_link_predictor(model, train_data, val_data, optimizer, criterion)

test_auc = eval_link_predictor(model, test_data)
print(f"Test: {test_auc:.3f}")

tomonori-masui

import torch_geometric.transforms as T

split = T.RandomNodeSplit(num_val=0.1, num_test=0.2)
graph = split(graph)

tomonori-masui

import random
from torch_geometric.utils import to_networkx
import networkx as nx

def convert_to_networkx(graph, n_sample=None):

    g = to_networkx(graph, node_attrs=["x"])
    y = graph.y.numpy()

    if n_sample is not None:

tomonori-masui

"""
0: inlier
1: contextual outlier only
2: structural outlier only
3: both contextual outlier and structural outlier
"""

Counter(graph.y.tolist())

tomonori-masui

import torch_geometric.transforms as T

split = T.RandomLinkSplit(
    num_val=0.05,
    num_test=0.1,
    is_undirected=True,
    add_negative_train_samples=False,
    neg_sampling_ratio=1.0,
)
train_data, val_data, test_data = split(graph)

tomonori-masui

from pygod.models import DOMINANT
from sklearn.metrics import roc_auc_score, average_precision_score

def train_anomaly_detector(model, graph):
    return model.fit(graph)

def eval_anomaly_detector(model, graph):

    outlier_scores = model.decision_function(graph)
    auc = roc_auc_score(graph.y.numpy(), outlier_scores)

tomonori-masui

from pygod.utils import load_data

graph = load_data('inj_cora')

tomonori-masui

from sklearn.metrics import roc_auc_score
from torch_geometric.utils import negative_sampling


class Net(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super().__init__()
        self.conv1 = GCNConv(in_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, out_channels)

tomonori-masui

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

class GCN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(dataset.num_node_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, data):

tomonori-masui

from torch_geometric.datasets import Planetoid

dataset = Planetoid(root='/tmp/Cora', name='Cora')
graph = dataset[0]