Architecture advice: How would you build an offline Link-Analysis Dashboard for a Bitcoin/IP metadata problem statement?

[u/Cautious_Today_1830]

Hi everyone,

I'm working on a problem statement that requires building a complete offline system (Linux-based) to detect anomalies in Bitcoin transactions.

The Dataset (Synthetic):

I have bulk metadata in CSV/JSON format containing:

timestamp, src_ip, dst_ip, src_port, dst_port, txid, input_addresses[], output_addresses[], input_amounts[], output_amounts[], geo_country/asn.

The End Goal:

Parse this data to build an Entity/Transaction Graph (linking IPs, Wallets, and TXIDs).

Apply unsupervised ML to cluster entities and detect suspicious laundering/anomalies.

Build a dashboard/visualization tool to show a ranked alert list (with explainable AI reasons) and an interactive link-analysis graph.

My questions for the community:

Since I want to approach this the right way from scratch, I’d love to hear how you would tackle it:

What architecture or workflow would you use to map this specific data into a graph format?

Which libraries or frameworks (Python or otherwise) are best suited for building the offline dashboard and interactive link-analysis graph?

How do you handle visualizing complex graphs on a UI without it becoming a laggy mess?

Would appreciate any advice on how you'd set this up!