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utils.py
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utils.py
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import numpy as np
def build_subgraph(adj, n_hops, node_idx):
"""Build subgraph by adding n-hop neighbors of node_idx.
Args:
adj: [N, N] adjacency matrix
n_hops: number of hops to add
node_idx: node index to add neighbors for
Returns:
subgraph: [N, N] subgraph adjacency matrix
"""
subgraph = adj.copy()
for _ in range(n_hops):
subgraph = subgraph + subgraph @ adj
subgraph = (subgraph > 0).astype(np.float32)
return subgraph * adj # only keep edges in original graph
def bandit_sampler_exp3(adj, n_hops, node_idx, n_samples, gamma=0.1):
"""Sample n_samples nodes from the subgraph of node_idx.
Args:
adj: [N, N] adjacency matrix
n_hops: number of hops to add
node_idx: node index to add neighbors for
n_samples: number of samples to take
gamma: parameter for exp3 algorithm
Returns:
samples: [n_samples] sampled node indices
"""
subgraph = build_subgraph(adj, n_hops, node_idx)
n_nodes = subgraph.shape[0]
degrees = subgraph.sum(1)
weights = np.ones(n_nodes) / n_nodes
samples = []
for _ in range(n_samples):
# sample node
node = np.random.choice(n_nodes, p=weights)
samples.append(node)
# update weights
weights = weights * np.exp(-gamma * subgraph[node] / degrees[node])
weights = weights / weights.sum()
return samples
def bandit_sampler_dep_round(adj, n_hops, node_idx, n_samples):
"""Sample n_samples nodes from the subgraph of node_idx.
Args:
adj: [N, N] adjacency matrix
n_hops: number of hops to add
node_idx: node index to add neighbors for
n_samples: number of samples to take
Returns:
samples: [n_samples] sampled node indices
"""
subgraph = build_subgraph(adj, n_hops, node_idx)
n_nodes = subgraph.shape[0]
degrees = subgraph.sum(1)
weights = np.ones(n_nodes) / n_nodes
samples = []
for _ in range(n_samples):
# sample node
node = np.random.choice(n_nodes, p=weights)
samples.append(node)
# update weights
weights = weights * (1 - subgraph[node] / degrees[node])
weights = weights / weights.sum()
return samples