Most on-chain tools stop at 'listing the data', while Bubblemaps V2 directly visualizes 'behavior patterns'—not just showing who the big players are, but telling you which addresses form suspicious relationship networks and when those 'invisible hands' moved in the same direction.
One, data ingestion and indexing
The engine fetches the holder snapshots, transaction relationships, and LP share changes from multiple chain nodes, performing deduplication and format standardization (address normalization/entity hints) first, and caching the 'Top N Holders List' to reduce repeated RPC queries. This layer outputs a structured draft of 'node-edge' for downstream feature engineering. The key here is not the data volume, but the temporal accuracy and recomputability; V2 will increase the update frequency to make the graph closer to the rhythm of event trading.
Two, behavioral features and cluster modeling (Magic Nodes)
The default bubble chart presents 'position size = node size; transfer relationship = connection'; however, insider control often hides behind 'multiple address avatars + short-term same direction'. After preprocessing, Magic Nodes introduces behavioral features (same source funds, time proximity, interaction frequency, common counterparties), using graph community discovery and association weights to cluster avatar groups into behavioral clusters, highlighting them as 'magic nodes'. This transforms 'the top 200 scattered into a piece' into an interpretable block structure in an instant.
Three, time series and narrative validation (Time Travel)
Time Travel replays the same graph along the timeline: deployment, distribution, listing, price increase, delivery, presented in segments. Researchers can align the 'announcement timing' with 'fund inflow' to verify whether volume precedes signals. For meme coins and short-term themes, this is better at filtering out the 'practice drills before the pump' routines than any single indicator. Tiger Brokers
Four, why this is more effective than just looking at candlesticks
Price is the result, distribution is the cause. When a breakout is accompanied by 'decreased concentration and new funds pouring in from multiple points', the sustainability is usually better; if Magic Nodes shows the main cluster reversing out during the breakout period, the risk of the bullish narrative failing increases. This turns 'whether to chase highs' into a verifiable structural judgment.
The value of V2 lies in integrating data engineering, graph theory, and frontend interaction into a decision language. For traders, this is preemptive risk control; for project parties, this is a public audit panel of decentralized commitments.
