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"""
Graph builder module for converting GDELT data to graph formats
"""
import pandas as pd
import networkx as nx
import json
class GraphBuilder:
"""Base class for building graph from GDELT data"""
def process_entities(self, row):
"""Process entities from a row and return nodes and relationships"""
nodes = []
relationships = []
event_id = row["GKGRECORDID"]
event_date = row["DATE"]
event_source = row["SourceCommonName"]
event_document_id = row["DocumentIdentifier"]
# event_image = row["V2.1SharingImage"] if pd.notna(row["V2.1SharingImage"]) else ""
event_quotations = row["V2.1Quotations"] if pd.notna(row["V2.1Quotations"]) else ""
event_tone = float(row["tone"]) if pd.notna(row["tone"]) else 0.0
# Add event node
nodes.append({
"id": event_id,
"type": "event",
"properties": {
"date": event_date,
"source": event_source,
"document": event_document_id,
# "image": event_image,
"quotations": event_quotations,
"tone": event_tone
}
})
# Process each entity type
entity_mappings = {
"V2EnhancedPersons": ("Person", "MENTIONED_IN"),
"V2EnhancedOrganizations": ("Organization", "MENTIONED_IN"),
"V2EnhancedLocations": ("Location", "LOCATED_IN"),
"V2EnhancedThemes": ("Theme", "CATEGORIZED_AS"),
"V2.1AllNames": ("Name", "MENTIONED_IN"),
"V2.1Counts": ("Count", "MENTIONED_IN"),
"V2.1Amounts": ("Amount", "MENTIONED_IN"),
}
for field, (label, relationship) in entity_mappings.items():
if pd.notna(row[field]):
entities = [e.strip() for e in row[field].split(';') if e.strip()]
for entity in entities:
nodes.append({
"id": entity,
"type": label.lower(),
"properties": {"name": entity}
})
relationships.append({
"from": entity,
"to": event_id,
"type": relationship,
"properties": {"created_at": event_date}
})
return nodes, relationships
class NetworkXBuilder(GraphBuilder):
"""Builder for NetworkX graphs"""
def build_graph(self, df):
G = nx.Graph()
for _, row in df.iterrows():
nodes, relationships = self.process_entities(row)
# Add nodes
for node in nodes:
G.add_node(node["id"],
type=node["type"],
**node["properties"])
# Add relationships
for rel in relationships:
G.add_edge(rel["from"],
rel["to"],
relationship=rel["type"],
**rel["properties"])
return G
class Neo4jBuilder(GraphBuilder):
def __init__(self, uri, user, password):
self.driver = GraphDatabase.driver(uri, auth=(user, password))
self.logger = logging.getLogger(__name__)
def close(self):
self.driver.close()
def build_graph(self, df):
with self.driver.session() as session:
for _, row in df.iterrows():
nodes, relationships = self.process_entities(row)
# Create nodes and relationships in Neo4j
try:
session.execute_write(self._create_graph_elements,
nodes, relationships)
except Exception as e:
self.logger.error(f"Error processing row {row['GKGRECORDID']}: {str(e)}")
def _create_graph_elements(self, tx, nodes, relationships):
# Create nodes
for node in nodes:
query = f"""
MERGE (n:{node['type']} {{id: $id}})
SET n += $properties
"""
tx.run(query, id=node["id"], properties=node["properties"])
# Create relationships
for rel in relationships:
query = f"""
MATCH (a {{id: $from_id}})
MATCH (b {{id: $to_id}})
MERGE (a)-[r:{rel['type']}]->(b)
SET r += $properties
"""
tx.run(query,
from_id=rel["from"],
to_id=rel["to"],
properties=rel["properties"])
class StreamlitGraphBuilder:
"""Adapted graph builder for Streamlit visualization"""
def __init__(self):
self.G = nx.Graph()
def process_row(self, row):
"""Process a single row of data"""
event_id = row["GKGRECORDID"]
event_props = {
"type": "event", # already in lowercase
"date": row["DATE"],
"source": row["SourceCommonName"],
"document": row["DocumentIdentifier"],
"tone": row["tone"],
# Store display name in its original format if needed.
"name": row["SourceCommonName"]
}
self.G.add_node(event_id, **event_props)
# Use lowercase node types for consistency in lookups.
entity_types = {
"V2EnhancedPersons": ("person", "MENTIONED_IN"),
"V2EnhancedOrganizations": ("organization", "MENTIONED_IN"),
"V2EnhancedLocations": ("location", "LOCATED_IN"),
"V2EnhancedThemes": ("theme", "CATEGORIZED_AS"),
"V2.1AllNames": ("name", "MENTIONED_IN"),
"V2.1Counts": ("count", "MENTIONED_IN"),
"V2.1Amounts": ("amount", "MENTIONED_IN"),
}
for col, (node_type, rel_type) in entity_types.items():
if pd.notna(row[col]):
# The actual display value (which may be in Parent Case) is preserved in the "name" attribute.
entities = [e.strip() for e in row[col].split(';') if e.strip()]
for entity in entities:
self.G.add_node(entity, type=node_type, name=entity)
self.G.add_edge(entity, event_id,
relationship=rel_type,
date=row["DATE"])
class StLinkBuilder(GraphBuilder):
"""Builder for st-link-analysis compatible graphs"""
def build_graph(self, df):
"""Build graph in st-link-analysis format"""
all_nodes = []
all_edges = []
edge_counter = 0
# Track nodes we've already added to avoid duplicates
added_nodes = set()
for _, row in df.iterrows():
nodes, relationships = self.process_entities(row)
# Process nodes
for node in nodes:
if node["id"] not in added_nodes:
stlink_node = {
"data": {
"id": str(node["id"]),
"label": node["type"].upper(),
**node["properties"]
}
}
all_nodes.append(stlink_node)
added_nodes.add(node["id"])
# Process relationships/edges
for rel in relationships:
edge_counter += 1
stlink_edge = {
"data": {
"id": f"e{edge_counter}",
"source": str(rel["from"]),
"target": str(rel["to"]),
"label": rel["type"],
**rel["properties"]
}
}
all_edges.append(stlink_edge)
return {
"nodes": all_nodes,
"edges": all_edges
}
def write_json(self, graph_data, filename):
"""Write graph to JSON file"""
with open(filename, 'w') as f:
json.dump(graph_data, f, indent=2) |