Roadmap & Open Questions
The LN Gossip Visualizer is a prototype. Here’s where we see it going.
Multi-Observer Deployment
Currently, we observe from a single vantage point. A single observer can rank peers by arrival time, but it can’t definitively identify message origins — it only sees who delivered the message to it first.
With multiple observers deployed across the network (different geographic regions, different peer sets), we could:
- Triangulate message origins by correlating arrival times across observers
- Map propagation paths more accurately
- Distinguish between “fast because well-connected” and “fast because close to the origin”
Real-Time Mode
The current visualizer works with pre-recorded data. A natural evolution is a live mode that streams gossip events in real time:
- Watch messages propagate as they happen
- Alert on anomalous propagation patterns
- Monitor network health continuously
Better Community Detection
Our current community assignment is largely manual (~15 known pubkeys). Future work should incorporate:
- Graph-based community detection using the channel graph topology (Louvain, label propagation)
- Implementation fingerprinting to automatically group peers by software (LND vs CLN vs Eclair vs LDK)
- Clustering by timing patterns — peers with similar propagation profiles likely share network characteristics
Minisketch & Set Reconciliation
The Lightning Network is exploring Minisketch-based gossip (Erlay-style set reconciliation) as a more bandwidth-efficient alternative to flood-fill gossip. Observing how this changes propagation dynamics would be valuable:
- Does set reconciliation make propagation more uniform?
- Does it reduce the information available to passive observers?
- How does it interact with different implementation strategies?
Open Research Questions
- How many observers are needed to reliably identify message origins?
- Can random forwarding delays effectively prevent timing analysis?
- What is the minimum connectivity an observer needs to get meaningful propagation data?
- How does gossip propagation change over time — is the network getting faster, slower, or more centralized?