From 32a9a3b2846c5128bf0204576774d06657b05424 Mon Sep 17 00:00:00 2001
From: Mohamed feroz khan D <cb.en.p2cys21017@cb.students.amrita.edu>
Date: Thu, 6 Jul 2023 18:38:48 +0530
Subject: [PATCH] Added README.md file
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1 file changed, 151 insertions(+)
create mode 100644 Assets/Temporal_Graph/Bitcoin/Visualization/Visualization_with_UTC/README.md
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+# Blockchain Forensics using Graph Temporal Logic
+    <br/>
+
+
+## Visualization with UTC
+### Code
+```
+import requests
+import pandas as pd
+from datetime import datetime
+import pathpy as pp
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+def fetch_address_data(address):
+ """
+ Function to fetch address data.
+
+ :param address: The address to fetch data for.
+ :return: The fetched data.
+ """
+ url = f"https://blockchain.info/address/{address}?format=json"
+ response = requests.get(url)
+ data = response.json()
+ return data
+
+
+def transform_data(data):
+ """
+ Function to transform address data.
+
+ :param data: The address data to transform.
+ :return: The transformed data.
+ """
+ transactions = []
+ for tx in data["txs"]:
+ for out in tx["out"]:
+ address_a = tx["inputs"][0]["prev_out"]["addr"]
+ address_b = out["addr"]
+ timestamp = datetime.fromtimestamp(tx["time"]).strftime("%m-%d-%Y %H:%M")
+ transaction_id = tx["hash"]
+ transaction = {
+ "Address A": address_a,
+ "Address B": address_b,
+ "Timestamp": timestamp,
+ "Transaction ID": transaction_id
+ }
+ transactions.append(transaction)
+ return transactions
+
+
+def export_to_excel(data):
+ """
+ Function to export data to Excel.
+
+ :param data: The data to export.
+ """
+ df = pd.DataFrame(data)
+ df["Timestamp"] = pd.to_datetime(df["Timestamp"]) # Convert Timestamp column to datetime
+ df["Timestamp"] = df["Timestamp"].dt.strftime("%m-%d-%Y %H:%M") # Format Timestamp column
+ filename = "Data.xlsx" # Change the filename to "Data.xlsx"
+ df.to_excel(filename, index=False)
+ print(f"Data exported to {filename}")
+
+
+def create_and_visualize_graph(df, user_address):
+ """
+ Function to create graph and visualize it.
+
+ :param df: The DataFrame containing the data.
+ :param user_address: The user's address.
+ """
+ network = pp.TemporalNetwork() # Create a temporal network from the DataFrame
+ edge_attributes = {} # Create a dictionary to store edge attributes
+
+ for _, row in df.iterrows():
+ source = str(row['Address A'])
+ target = str(row['Address B'])
+ timestamp_str = str(row['Timestamp'])
+ transaction_hash = str(row['Transaction ID'])
+
+ timestamp = datetime.strptime(timestamp_str, '%m-%d-%Y %H:%M')
+ timestamp_unix = int(timestamp.timestamp())
+
+ network.add_edge(source, target, ts=timestamp_unix)
+ edge_attributes[(source, target)] = {
+ 'timestamp': timestamp_unix,
+ 'transaction_hash': transaction_hash
+ }
+
+ graph = nx.DiGraph() # Create a networkx graph
+
+ for node in network.nodes:
+ if node == user_address: # Highlight user address node in red
+ graph.add_node(node, color='red')
+ else:
+ graph.add_node(node, color='skyblue')
+
+ for source, target, timestamp in network.tedges:
+ transaction_hash = edge_attributes[(source, target)]['transaction_hash']
+ if source == user_address or target == user_address: # Highlight edges connected to user address in blue
+ graph.add_edge(source, target, timestamp=timestamp, transaction_hash=transaction_hash, color='blue')
+ else:
+ graph.add_edge(source, target, timestamp=timestamp, transaction_hash=transaction_hash, color='gray')
+
+ plt.figure(figsize=(10, 6)) # Draw the graph using matplotlib
+ pos= nx.spring_layout(graph)
+ node_colors = [graph.nodes[node]['color'] for node in graph.nodes]
+ edge_colors = [graph.edges[edge]['color'] for edge in graph.edges]
+ nx.draw_networkx(graph, pos, with_labels=True, node_size=500, font_size=8, node_color=node_colors,
+ edge_color=edge_colors)
+ edge_labels = nx.get_edge_attributes(graph, 'timestamp')
+ nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels, font_size=6)
+ plt.tight_layout() # Show the graph
+ plt.show()
+
+
+def main():
+ """Main program."""
+ while True:
+ user_address = input("Enter the address (or 'q' to quit): ")
+
+ if user_address.lower() == 'q':
+ break
+
+ address_data = fetch_address_data(user_address)
+ transformed_data = transform_data(address_data)
+ export_to_excel(transformed_data)
+ df = pd.read_excel('Data.xlsx')
+ create_and_visualize_graph(df, user_address)
+
+
+if __name__ == '__main__':
+ main()
+```
+
+### Output
+<p align="center">
+<img src="Temporal_Graph_Visualization_Actual.png" width="800"/>
+</p>
+
+<p align="center">
+<img src="Temporal_Graph_Visualization_Zoom.png" width="800"/>
+</p>
+
+### Source
+<p align="center">
+<img src="chainabuse_Reported_Address.png" width="800"/>
+</p>
+
--
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