graph-construct/construct-graph.ipynb: add
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graph-construct/construct-graph.ipynb
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graph-construct/construct-graph.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Construct interaction graphs\n",
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"\n",
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"Constructing interaction graphs and counting the neighbour interactions for each user."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import the dataset and create user nodes dictionary."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"\n",
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"case = '...'\n",
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"casegraphs = '...'\n",
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"\n",
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"df_data = pd.read_csv(f'../datasets/{case}.csv')\n",
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"user_nodes = df_data.groupby('user')['id'].apply(list).to_dict()\n",
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"\n",
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"df_data.info()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Construct and export nearest-neighbour graph and count nearest-neighbours in nearest-neighbour graph."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import networkx as nx\n",
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"\n",
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"# Define undirected graph\n",
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"G = nx.Graph()\n",
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"\n",
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"# Construct nearest-neighbour graph\n",
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"for _, row in df_data.iterrows():\n",
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" G.add_node(row['id'], user=row['user'], sentiment=row['s'], text=row['text'])\n",
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" if row['parentid'] != 'no_parent':\n",
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" G.add_edge(row['id'], row['parentid'])\n",
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"\n",
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"# Export nearest-neighbour graph\n",
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"nx.write_graphml(G, f'../datasets/{casegraphs}-nn-graph.graphml')\n",
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"\n",
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"# Define nearest-neighbour interaction count dictionary\n",
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"nn_count = {}\n",
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"\n",
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"# Count nearest neighbours for each user\n",
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"for node in G.nodes(data=True):\n",
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" node_id, attributes = node\n",
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" user = attributes['user']\n",
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"\n",
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" count = len(set(G.neighbors(node_id)) - set(user_nodes[user]))\n",
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"\n",
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" if user in nn_count:\n",
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" nn_count[user] += count\n",
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" else:\n",
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" nn_count[user] = count"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Construct and export transitive-closure graph and count nearest-neighbours in transitive-closure graph."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Construct transitive-closure graph\n",
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"tc_G = nx.transitive_closure(G)\n",
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"\n",
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"# Export transitive-closure graph\n",
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"nx.write_graphml(tc_G, f'../datasets/{casegraphs}-tc-graph.graphml')\n",
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"\n",
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"# Define transitive-closure interaction count dictionary\n",
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"tc_count = {}\n",
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"\n",
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"# Count neighbours for each user\n",
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"for node in tc_G.nodes(data=True):\n",
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" node_id, attributes = node\n",
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" user = attributes['user']\n",
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"\n",
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" count = len(set(tc_G.neighbors(node_id)) - set(user_nodes[user]))\n",
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"\n",
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" if user in tc_count:\n",
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" tc_count[user] += count\n",
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" else:\n",
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" tc_count[user] = count"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Construct and export directed-transitive-closure graph and count nearest-neighbours in directed-transitive-closure graph."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define directed graph\n",
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"dG = nx.DiGraph()\n",
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"\n",
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"# Construct nearest-neighbour directed graph\n",
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"for _, row in df_data.iterrows():\n",
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" dG.add_node(row['id'], user=row['user'], sentiment=row['s'], text=row['text'])\n",
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" if row['parentid'] != 'no_parent':\n",
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" dG.add_edge(row['id'], row['parentid'])\n",
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"\n",
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"nx.write_graphml(dG, f'../datasets/{casegraphs}-nn-dgraph.graphml')\n",
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"\n",
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"# Construct directed-transitive-closure directed graph\n",
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"dtc_dG = nx.transitive_closure(dG)\n",
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"\n",
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"# Export directed-transitive-closure directed graph\n",
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"nx.write_graphml(dtc_dG, f'../datasets/{casegraphs}-dtc-dgraph.graphml')\n",
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"\n",
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"# Convert to directed-transitive-closure undirected graph\n",
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"dtc_G = dtc_dG.to_undirected()\n",
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"\n",
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"# Export directed-transitive-closure graph\n",
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"nx.write_graphml(dtc_G, f'../datasets/{casegraphs}-dtc-graph.graphml')\n",
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"\n",
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"# Define directed-transitive-closure interaction count dictionary\n",
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"dtc_count = {}\n",
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"\n",
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"# Count neighbours for each user\n",
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"for node in dtc_G.nodes(data=True):\n",
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" node_id, attributes = node\n",
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" user = attributes['user']\n",
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"\n",
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" count = len(set(dtc_G.neighbors(node_id)) - set(user_nodes[user]))\n",
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"\n",
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" if user in dtc_count:\n",
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" dtc_count[user] += count\n",
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" else:\n",
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" dtc_count[user] = count"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Construct and export user interaction count dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"user_c = df_data.groupby('user')['id'].count().to_dict()\n",
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"user_s = df_data.groupby('user')['s'].mean().to_dict()\n",
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"user_stds = df_data.groupby('user')['s'].std().to_dict()\n",
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"\n",
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"df_users = pd.DataFrame({\n",
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" 'nn-count': pd.Series(nn_count), \n",
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" 'dtc-count': pd.Series(dtc_count), \n",
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" 'tc-count': pd.Series(tc_count),\n",
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" 'mean-sentiment': pd.Series(user_s),\n",
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" 'std-sentiment': pd.Series(user_stds),\n",
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" 'post-count': pd.Series(user_c)\n",
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"})\n",
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"\n",
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"export_dataset = f'../datasets/{case}-users.csv'\n",
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"df_users.to_csv(export_dataset, index=False)\n",
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"\n",
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"df_users.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_users.describe()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "8c75c0fdd1a718867cdcb84b32adcfdbeaad00b3a4e00a59385211aeed084d4c"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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