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Reddit/processed/reddit_graph.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8aed3368b44889670caa82468ea6c78944c6eede3a03891c6b70b56d137db70f
+size 134

Reddit/raw/68841_tweets_multiclasses_filtered_0722_part1.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdc595c36f74073feeb9dea9af01a467dd64743ceec15442085d8c3f2f187339
+size 20623408

Reddit/raw/Reddit-processing.py

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+import os
+import pandas as pd
+import math
+import pickle as pkl
+import torch
+from torch_geometric.data import Data
+
+# Get the directory of the current script
+script_dir = os.path.dirname(os.path.abspath(__file__))
+base_dir = os.path.dirname(script_dir)
+raw_dir = os.path.join(base_dir, 'processed/original')
+
+# Define the file path
+reddit_path = os.path.join(raw_dir, 'reddit_1m.csv')
+
+# Read the Reddit data
+df = pd.read_csv(reddit_path)
+print(df.shape)
+
+# Select required columns
+df_graph = df[['subreddit_id', 'subreddit', 'name', 'body', 'score', 'author', 'author_flair_text', 'distinguished']]
+df_graph.rename(columns={'name': 'post_id',
+                         'body': 'post',
+                         'author': 'user',
+                         'author_flair_text': 'user_flair'},
+                inplace=True, errors='raise')
+
+# Drop duplicates, deleted posts, and rows with NaN post_id
+df_graph = df_graph.drop_duplicates()
+df_graph = df_graph[df_graph['post'] != '[deleted]']
+df_graph = df_graph.dropna(subset=['post_id'])
+print(df_graph.shape)
+print(df_graph['post_id'].nunique())
+
+# Encode distinguished and user_flair
+df_graph['distinguished'] = df_graph['distinguished'].apply(lambda x: 0 if pd.isna(x) else 1)
+df_graph['user_flair'] = df_graph['user_flair'].apply(lambda x: "" if pd.isna(x) else x)
+
+text_nodes = []
+
+# Create sub_id2idx
+sub_id2idx = {}
+sub_nodes = []
+for _, row in df_graph.iterrows():
+    sub_id = row['subreddit_id']
+    if sub_id not in sub_nodes:
+        sub_id2idx[sub_id] = len(sub_nodes)
+        sub_nodes.append(sub_id)
+        text_nodes.append(row['subreddit'])
+node_labels = [-1] * len(sub_nodes)  # No labels
+
+print("Length of sub nodes:", len(sub_nodes))
+print("Sample sub node labels:", node_labels[:5])
+print("Sample sub node texts:", text_nodes[:5])
+
+# Create user_n2idx
+user_n2idx = {}  # Username to index mapping
+user_nodes = []
+for _, row in df_graph.iterrows():
+    user_n = row['user']
+    if user_n in user_nodes:  # Existing user: add new flair and update label
+        if row['user_flair'] not in text_nodes[user_n2idx[user_n]]:
+            text_nodes[user_n2idx[user_n]] += "\n" + row['user_flair']
+        node_labels[user_n2idx[user_n]] = max(row['distinguished'], node_labels[user_n2idx[user_n]])
+    else:  # New user: add the user to user_n2idx
+        user_n2idx[user_n] = len(user_nodes) + len(sub_nodes)
+        user_nodes.append(user_n)
+        text_nodes.append(row['user_flair'])
+        node_labels.append(row['distinguished'])
+
+print("Length of user nodes:", len(user_nodes))
+print("Sample user node labels:", node_labels[-10:])
+print("Sample user node texts:", text_nodes[-10:])
+
+# Record edge information
+edge_index = [[], []]
+text_edges = []
+edge_scr_labels = []  # Continuous score
+edge_spe_labels = []  # Binary special label
+all_edges = set()
+
+for _, row in df_graph.iterrows():
+    user_idx = user_n2idx[row['user']]
+    sub_idx = sub_id2idx[row['subreddit_id']]
+
+    if (user_idx, sub_idx) not in all_edges:  # Only keep one edge between two nodes
+        edge_index[0].append(user_idx)
+        edge_index[1].append(sub_idx)
+
+        text_edges.append(row['post'])
+        edge_scr_labels.append(row['score'])
+        edge_spe_labels.append(row['distinguished'])
+
+        all_edges.add((user_idx, sub_idx))
+
+print("Length of edges:", len(edge_index[0]))
+print("Sample edge score labels:", edge_scr_labels[-10:])
+print("Sample edge special labels:", edge_spe_labels[-10:])
+print("Sample edge texts:", text_edges[-10:])
+
+edge_scr_labels = [0 if math.isnan(x) else x for x in edge_scr_labels]
+edge_spe_labels = [0 if math.isnan(x) else x for x in edge_spe_labels]
+
+# Save as torch data
+graph = Data(
+    text_nodes=text_nodes,
+    text_edges=text_edges,
+    node_labels=torch.tensor(node_labels, dtype=torch.long),
+    edge_index=torch.tensor(edge_index, dtype=torch.long),
+    edge_score_labels=torch.tensor(edge_scr_labels, dtype=torch.long),
+    edge_special_labels=torch.tensor(edge_spe_labels, dtype=torch.long),
+)
+
+output_file = os.path.join(base_dir, 'output/reddit_graph.pkl')
+with open(output_file, 'wb') as file:
+    pkl.dump(graph, file)
+
+print(f"Data processing complete. Processed data saved to: {output_file}")

Reddit/raw/download_data.sh

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+#!/bin/bash
+
+# Get the directory of the current script
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+BASE_DIR="$(dirname "$SCRIPT_DIR")"
+RAW_DIR="$BASE_DIR/raw"
+
+# Create the raw directory
+mkdir -p "$RAW_DIR"
+
+# Define URLs of the files to be downloaded
+urls=(
+    "https://github.com/YuweiCao-UIC/KPGNN/raw/main/datasets/Twitter/68841_tweets_multiclasses_filtered_0722_part1.npy"
+)
+
+# Download each file to the raw directory
+for url in "${urls[@]}"; do
+    wget -P "$RAW_DIR" "$url"
+done
+
+echo "Download complete."
+
+
+