Bitcoin LN node insight

Develop your own data-driven Lightning Network insight

Discover network-wide statistics on nodes, interactively explore node local networks, measure the impact of opening or closing a channel, and identify potentially profitable paths in the network

Histogram
Scatterplot

Choose a variable

Choose an X-axis variable

Choose a Y-axis variable

Simulate either a rebalance or a payment

Rebalance

Payment

Step 1: enter a pubkey/alias on which to simulate a circular rebalance

Step 2: enter/choose an outgoing node
*Can be a node with an existing channel to the one in Step 1, otherwise a channel will be simulated

Step 3: enter/select an incoming node
*Can be a node with an existing channel to the one in Step 1, otherwise a channel will be simulated

Step 1: enter/choose a node to use as first hop for a payment

Step 2: enter/select a node to use as payment destination

Summary stats

Enter pubkey/alias to view local peer network

Peer network
Summary table

Step 1: enter your pubkey or alias

See node stats on

Step 2: enter or select pubkey/alias of up to 3 nodes with which to simulate adding or removing channels

Add

Remove

Add

Remove

Add

Remove

Optional: apply filters to pubkey or alias menu choices

Filter by range of total capacity (in bitcoin)

Filter by range average channel capacity (in bitcoin)

Filter by range of total channels

Filter by range of median channel fee rates (ppm)

Filter by range of approximate node age (in days)

Filter by range of betweenness centrality ranks

Filter by range of closeness centrality ranks

Filter by range of eigenvector centrality ranks

Filter by one or more communities

Show pubkey or alias or both in the drop-down menu

Pubkey

Alias

Both

Peer overlap

Node centrality ranks

Centralization scores of the network

Contact

Email / Telegram / Lightning node

Source code

See the code here .

Betweenness centrality

Betweeness centrality measures the number of shortest paths that pass through a node. A higher number of shortest paths a node has to any two other node in the network, the more likely they will be included in a route depending on the liquidity balance of each channel in the path.

Closeness/hopness centrality

Closeness/hopness centrality is a measure of how many hops it takes to reach any node on the network from a given node. The better the rank, the fewer the hops required to reach any and all nodes.

Eigenvector/hubness centrality

Eigenvector/hubness centrality measures influence of a given node in the network. Higher ranks imply a well-connected node that is linked to other well-connected nodes. A lower eigenvector centrality could also imply a new and/or underserved node in the network.

Maximum liquidity flow

Maximum flow is the highest amount of sats that can theoretically be pushed through a path if liquidity were 100% outbound. In reality, outbound across a path is likely 50% or less.

Community

The communities are inferred with the Louvain algorithm. It detects clusters of nodes. It could be a useful metric to identify groups of nodes further away from a given node in the network.