BMT Series (Twenty-Six): Identification and Interpretation of On-Chain Cluster Patterns
In on-chain data analysis, cluster patterns always fascinate me. It’s like an invisible map, connecting those seemingly independent wallet addresses. The core tool of Bubblemaps—the bubble chart—is used to identify and interpret these patterns. Simply put, when there are frequent transfers or indirect connections between multiple holders, clusters are formed. This is often not random; there are stories behind it, such as funding distribution paths, internal operations of teams, or some techniques to evade tracking.
Taking SHIB as an example, the default bubble chart displays the top 250 holders, with the size of each bubble representing the amount held, and the links indicating transfer history. But if you only look at the surface, many connections will be overlooked. At this point, the magic nodes become key, as they can automatically expand those intermediary addresses that do not hold tokens but serve as bridges. The result? The originally scattered bubbles suddenly connect into one, forming a clear cluster. For instance, a central address distributes tokens to multiple peripheral addresses, which could be a VC allocating shares; or links are dense but amounts small, which might indicate the team testing the network.
Interpreting these patterns requires some experience. I usually start with the time travel feature, tracing back to the token issuance date, when the clusters were at their most primitive, allowing us to see traces of early capital flows. Combined with AI models, it can provide preliminary explanations, such as “this cluster may involve multi-signature wallets,” or “suspected associated addresses used for dispersing holdings.” In practical operations, I recommend first enabling the magic nodes to see hidden connections; then comparing changes at different points in time. If the cluster spreads quickly after issuance, it might be a market promotion signal; if it suddenly shrinks, it could indicate dumping risks.
Cross-chain data is also crucial. Bubblemaps supports multi-chains such as Solana and BNB Chain, allowing you to track funds jumping from one chain to another, capturing broader patterns. Remember, clusters are not data noise, but intelligence sources. Learn to recognize them, and you can benefit from InfoFi. Bubblemaps V2 opens these features for free; try analyzing a new token, starting from the clusters—you’ll surely uncover surprises.
