Add timestamped_graph.md describing the use case of timestamped and future possibilities

Author: Susmita-Chakrabarty

timestamped_graph.md

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+# Timestamped Graph Snapshots
+
+## Overview
+
+The timestamped snapshot feature enables graphs to store their historical states automatically using real-time timestamps. This allows users to track the evolution of a graph over time, retrieve past states, and perform time-based analysis.
+
+## Why This Feature Matters
+
+- **Graph Evolution Tracking**: Enables users to analyze how a graph changes over time.
+- **Anomaly Detection in Secure Networks**: Helps in detecting unusual patterns in cryptographic protocols and secure transactions.
+- **Time-Series Graph Analysis**: Supports applications in secure financial transactions and privacy-preserving communications.
+- **Cryptographic Security (Future Enhancement)**: Can be extended to sign snapshots using HMAC for integrity verification and encrypted storage.
+
+## How It Works
+
+### **Snapshot Storage**
+
+- When `add_snapshot()` is called, a deep copy of the graph is saved with a unique timestamp.
+- Snapshots are stored in an internal dictionary where timestamps serve as keys.
+
+### **Retrieving Historical States**
+
+- Users can retrieve past versions of the graph using `get_snapshot(timestamp)`, enabling time-based queries.
+- If an invalid timestamp is requested, the system raises a clear error with available timestamps.
+
+### **Listing Available Snapshots**
+
+- The `list_snapshots()` method provides a sorted list of all saved timestamps.
+
+## Usage Example
+
+```python
+from pydatastructs.graphs import Graph
+
+graph = Graph(implementation='adjacency_list')
+
+graph.add_edge("A", "B", weight=5)
+graph.add_snapshot()  # Snapshot stored with real-time timestamp
+
+graph.add_edge("B", "C", weight=7)
+graph.add_snapshot()
+
+# List stored snapshots
+print(graph.list_snapshots())  # Output: [timestamp1, timestamp2]
+
+# Retrieve a past graph state
+old_graph = graph.get_snapshot(graph.list_snapshots()[0])
+## Future Enhancements
+
+- **Secure Graph Snapshots for Banking & Finance**: Implement HMAC or cryptographic signing to prevent unauthorized modifications in financial transaction networks.
+- **Encrypted Graph Storage for Privacy-Critical Applications**: Apply homomorphic encryption or privacy-preserving encryption to protect sensitive data, such as medical records, customer transactions, or identity graphs.
+- **Efficient Storage for Large-Scale Graphs**: Introduce optimized serialization techniques to store historical snapshots with minimal overhead, making it scalable for real-world enterprise applications.
+- **Integrity Verification for Regulatory Compliance**: Ensure snapshots cannot be altered without detection by integrating cryptographic hash functions. This is crucial for auditing in banking, supply chain security, and legal record-keeping.
+
+## Conclusion
+
+This feature lays the groundwork for advanced **cryptographic** graph analytics, allowing users to analyze, secure, and retrieve historical graph states efficiently. As future enhancements are implemented, timestamped snapshots will serve as a core foundation for **secure graph-based computations, privacy-preserving transactions, and cryptographic security in graph structures.**
+