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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "N20l3SsqSVUM"
},
"source": [
"## Leveraging DuckDB with HF Datasets - GDELT Global KG\n",
"\n",
"This notebook demonstrates how to seamlessly transform **GDELT** knowledge graph data into a coherent format that can be pushed to both **NetworkX** and **Neo4j**. It provides a **referenceable pipeline** for data professionals, researchers, and solution architects who need to:\n",
"\n",
"1. **Ingest and Query Data Efficiently** \n",
" - Utilize **DuckDB** to load just the required portions of large Parquet datasets, enabling targeted data exploration and analysis.\n",
" - It also allows for iteratively honing in on a specific segment of data using splits - helping to maximize performance / cost / efficiency.\n",
"\n",
"2. **Maintain Consistent Graph Modeling** \n",
" - Leverage a shared parsing and entity extraction layer to build consistent node and relationship structures in both an **in-memory** graph (NetworkX) and a **Neo4j** database. (not a requirement per se - but an approach I wanted to start with)\n",
"\n",
"3. **Run Advanced Queries and Analytics** \n",
" - Illustrate critical tasks like **centrality** and **community detection** to pinpoint influential nodes and groupings, and execute **Cypher** queries for real-time insights.\n",
"\n",
"4. **Visualize and Export** \n",
" - Produce simple web-based **PyVis** visualizations or **matplotlib** plots.\n",
" - more importantly the data can also be exported in **JSON** and GraphML for integration with other graph tooling. (D3.js, Cytoscape, etc.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DCPEB5tpfW44"
},
"outputs": [],
"source": [
"%pip install -q duckdb networkx pandas neo4j pyvis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A1vEyOkm7LPV"
},
"outputs": [],
"source": [
"from google.colab import userdata\n",
"\n",
"URI = userdata.get('NEO4J_URI')\n",
"USER = 'neo4j'\n",
"PASSWORD = userdata.get('NEO4J_PASSWORD')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cm8t66uPy_C7"
},
"outputs": [],
"source": [
"import duckdb\n",
"import networkx as nx\n",
"from neo4j import GraphDatabase\n",
"import logging\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"from pyvis.network import Network\n",
"\n",
"def get_gdelt_data(limit=100):\n",
" \"\"\"Get data from DuckDB with specified limit\"\"\"\n",
" con = duckdb.connect(database=':memory:')\n",
"\n",
" # Create view of the dataset\n",
" con.execute(\"\"\"\n",
" CREATE VIEW train AS (\n",
" SELECT *\n",
" FROM read_parquet('hf://datasets/dwb2023/gdelt-gkg-march2020-v2/*.parquet')\n",
" );\n",
" \"\"\")\n",
"\n",
" # Single query with limit\n",
" query = f\"\"\"\n",
" SELECT\n",
" GKGRECORDID,\n",
" DATE,\n",
" SourceCommonName,\n",
" DocumentIdentifier,\n",
" V2EnhancedPersons,\n",
" V2EnhancedOrganizations,\n",
" V2EnhancedLocations,\n",
" V2EnhancedThemes,\n",
" CAST(SPLIT_PART(\"V1.5Tone\", ',', 1) AS FLOAT) as tone\n",
" FROM train\n",
" LIMIT {limit}\n",
" \"\"\"\n",
"\n",
" results_df = con.execute(query).fetchdf()\n",
" con.close()\n",
" return results_df\n",
"\n",
"class GraphBuilder:\n",
" \"\"\"Base class for building graph from GDELT data\"\"\"\n",
" def process_entities(self, row):\n",
" \"\"\"Process entities from a row and return nodes and relationships\"\"\"\n",
" nodes = []\n",
" relationships = []\n",
" event_id = row[\"GKGRECORDID\"]\n",
" event_date = row[\"DATE\"]\n",
" event_source = row[\"SourceCommonName\"]\n",
" event_document_id = row[\"DocumentIdentifier\"]\n",
" event_tone = float(row[\"tone\"]) if pd.notna(row[\"tone\"]) else 0.0\n",
"\n",
" # Add event node\n",
" nodes.append({\n",
" \"id\": event_id,\n",
" \"type\": \"event\",\n",
" \"properties\": {\n",
" \"date\": event_date,\n",
" \"source\": event_source,\n",
" \"document\": event_document_id,\n",
" \"tone\": event_tone\n",
" }\n",
" })\n",
"\n",
" # Process each entity type\n",
" entity_mappings = {\n",
" \"V2EnhancedPersons\": (\"Person\", \"MENTIONED_IN\"),\n",
" \"V2EnhancedOrganizations\": (\"Organization\", \"MENTIONED_IN\"),\n",
" \"V2EnhancedLocations\": (\"Location\", \"LOCATED_IN\"),\n",
" \"V2EnhancedThemes\": (\"Theme\", \"CATEGORIZED_AS\")\n",
" }\n",
"\n",
" for field, (label, relationship) in entity_mappings.items():\n",
" if pd.notna(row[field]):\n",
" entities = [e.strip() for e in row[field].split(';') if e.strip()]\n",
" for entity in entities:\n",
" nodes.append({\n",
" \"id\": entity,\n",
" \"type\": label.lower(),\n",
" \"properties\": {\"name\": entity}\n",
" })\n",
" relationships.append({\n",
" \"from\": entity,\n",
" \"to\": event_id,\n",
" \"type\": relationship,\n",
" \"properties\": {\"created_at\": event_date}\n",
" })\n",
"\n",
" return nodes, relationships\n",
"\n",
"class NetworkXBuilder(GraphBuilder):\n",
" def build_graph(self, df):\n",
" G = nx.Graph()\n",
"\n",
" for _, row in df.iterrows():\n",
" nodes, relationships = self.process_entities(row)\n",
"\n",
" # Add nodes\n",
" for node in nodes:\n",
" G.add_node(node[\"id\"],\n",
" type=node[\"type\"],\n",
" **node[\"properties\"])\n",
"\n",
" # Add relationships\n",
" for rel in relationships:\n",
" G.add_edge(rel[\"from\"],\n",
" rel[\"to\"],\n",
" relationship=rel[\"type\"],\n",
" **rel[\"properties\"])\n",
"\n",
" return G\n",
"\n",
"class Neo4jBuilder(GraphBuilder):\n",
" def __init__(self, uri, user, password):\n",
" self.driver = GraphDatabase.driver(uri, auth=(user, password))\n",
" self.logger = logging.getLogger(__name__)\n",
"\n",
" def close(self):\n",
" self.driver.close()\n",
"\n",
" def build_graph(self, df):\n",
" with self.driver.session() as session:\n",
" for _, row in df.iterrows():\n",
" nodes, relationships = self.process_entities(row)\n",
"\n",
" # Create nodes and relationships in Neo4j\n",
" try:\n",
" session.execute_write(self._create_graph_elements,\n",
" nodes, relationships)\n",
" except Exception as e:\n",
" self.logger.error(f\"Error processing row {row['GKGRECORDID']}: {str(e)}\")\n",
"\n",
" def _create_graph_elements(self, tx, nodes, relationships):\n",
" # Create nodes\n",
" for node in nodes:\n",
" query = f\"\"\"\n",
" MERGE (n:{node['type']} {{id: $id}})\n",
" SET n += $properties\n",
" \"\"\"\n",
" tx.run(query, id=node[\"id\"], properties=node[\"properties\"])\n",
"\n",
" # Create relationships\n",
" for rel in relationships:\n",
" query = f\"\"\"\n",
" MATCH (a {{id: $from_id}})\n",
" MATCH (b {{id: $to_id}})\n",
" MERGE (a)-[r:{rel['type']}]->(b)\n",
" SET r += $properties\n",
" \"\"\"\n",
" tx.run(query,\n",
" from_id=rel[\"from\"],\n",
" to_id=rel[\"to\"],\n",
" properties=rel[\"properties\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ghbLZNLe23x1"
},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" # Get data once\n",
" df = get_gdelt_data(limit=25) # Get 25 records\n",
"\n",
" # Build NetworkX graph\n",
" nx_builder = NetworkXBuilder()\n",
" G = nx_builder.build_graph(df)\n",
"\n",
" # Print graph information\n",
" print(f\"NetworkX Graph Summary:\")\n",
" print(f\"Nodes: {G.number_of_nodes()}\")\n",
" print(f\"Edges: {G.number_of_edges()}\")\n",
"\n",
" # Print node types distribution\n",
" node_types = {}\n",
" for _, attr in G.nodes(data=True):\n",
" node_type = attr.get('type', 'unknown')\n",
" node_types[node_type] = node_types.get(node_type, 0) + 1\n",
"\n",
" print(\"\\nNode types distribution:\")\n",
" for ntype, count in node_types.items():\n",
" print(f\"{ntype}: {count}\")\n",
"\n",
" # Build Neo4j graph\n",
" neo4j_builder = Neo4jBuilder(URI, USER, PASSWORD)\n",
" try:\n",
" neo4j_builder.build_graph(df)\n",
" finally:\n",
" neo4j_builder.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mkJKz_soTsAY"
},
"outputs": [],
"source": [
"# run cypher query for validation\n",
"\n",
"from neo4j import GraphDatabase\n",
"\n",
"class Neo4jQuery:\n",
" def __init__(self, uri, user, password):\n",
" self.driver = GraphDatabase.driver(uri, auth=(user, password))\n",
" self.logger = logging.getLogger(__name__)\n",
"\n",
" def close(self):\n",
" self.driver.close()\n",
"\n",
" def run_query(self, query):\n",
" with self.driver.session() as session:\n",
" result = session.run(query)\n",
" return result.data()\n",
"\n",
"query_1 = \"\"\"\n",
"// Count nodes by type\n",
"MATCH (n)\n",
"RETURN labels(n) as type, count(*) as count\n",
"ORDER BY count DESC;\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mrlWADO93ize"
},
"outputs": [],
"source": [
"def visualize_graph(G, output_file='gdelt_network.html'):\n",
" \"\"\"Visualize NetworkX graph using Pyvis\"\"\"\n",
" # Create Pyvis network\n",
" net = Network(notebook=True,\n",
" height='750px',\n",
" width='100%',\n",
" bgcolor='#ffffff',\n",
" font_color='#000000')\n",
"\n",
" # Configure physics\n",
" net.force_atlas_2based(gravity=-50,\n",
" central_gravity=0.01,\n",
" spring_length=100,\n",
" spring_strength=0.08,\n",
" damping=0.4,\n",
" overlap=0)\n",
"\n",
" # Color mapping for node types\n",
" color_map = {\n",
" 'event': '#1f77b4', # Blue\n",
" 'person': '#00ff00', # Green\n",
" 'organization': '#ffa500', # Orange\n",
" 'location': '#ff0000', # Red\n",
" 'theme': '#800080' # Purple\n",
" }\n",
"\n",
" # Add nodes\n",
" for node, attr in G.nodes(data=True):\n",
" node_type = attr.get('type', 'unknown')\n",
" title = f\"Type: {node_type}\\n\"\n",
" for k, v in attr.items():\n",
" if k != 'type':\n",
" title += f\"{k}: {v}\\n\"\n",
"\n",
" net.add_node(node,\n",
" title=title,\n",
" label=str(node)[:20] + '...' if len(str(node)) > 20 else str(node),\n",
" color=color_map.get(node_type, '#gray'),\n",
" size=20 if node_type == 'event' else 15)\n",
"\n",
" # Add edges\n",
" for source, target, attr in G.edges(data=True):\n",
" net.add_edge(source,\n",
" target,\n",
" title=f\"{attr.get('relationship', '')}\\nDate: {attr.get('created_at', '')}\",\n",
" color='#666666')\n",
"\n",
" # Save visualization\n",
" net.show(output_file)\n",
" return f\"Graph visualization saved to {output_file}\"\n",
"\n",
"# Usage example:\n",
"if __name__ == \"__main__\":\n",
" visualize_graph(G)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RqFRO1atnIIT"
},
"outputs": [],
"source": [
"!pip show duckdb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "95ML8u0LnKif"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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