Skip to main content

​
Zonein 3D Network Graph

The 3D Network Graph turns raw on‑chain activity into a live, explorable map of relationships and money flows. You can see who is connected to whom, how funds move, and which wallets influence markets, without reading thousands of transactions. The scene updates in near real time and uses the same validated data and scoring you see in the dashboard, so views are consistent, explainable, and shareable. How the Recommendation Engine Works.png

​
From Data to 3D

​
Sources & enrichment

The graph draws from on‑chain transactions and logs across major chains, augmented with labels (exchanges, contracts, smart‑money sets), ENS or verified names where available, and optional social verifications. Each wallet becomes a node with attributes (balance bands, activity rate, profitability, influence score). Each interaction becomes an edge typed as: direct transfer, DEX swap, bridge, or social/ownership link.

​
Metrics & analytics

Stream jobs compute:
  • Flow metrics: net in/out by token and window; edge weight = value × frequency; direction via arrows.
  • Clusters: community detection groups related wallets; you can expand/collapse clusters.
  • Centrality & influence: who sits on many important paths and who tends to move price/volume shortly after transactions.
  • Risk signals: concentration, synchronized moves, suspicious patterns (rapid daisy‑chains, peel chains).

​
Layout & updates

Nodes arrange with a force‑directed layout tuned for stability as new edges arrive. Updates stream in incrementally; the scene rebalances without jolts. A time slider lets you view historical states (“time travel”) or isolate windows (e.g., last 1h). Path‑finding tools compute Shortest Path, Money Trail (multi‑hop fund traces), and Influence Path (likely propagation).

​
What You Use

​
Core controls

  • Navigate: orbit, pan, and zoom; double‑click to focus; press F to frame a selection.
  • Search & select: paste an address/ENS; click a node for its panel (address, labels, P&L bands, win rate, major holdings, recent activity, influence score).
  • Filter: time window, token, chain, wallet type (exchange, contract, smart money, whale), profitability band, activity level.
  • Trace: choose Money Trail to follow funds across hops; Shortest Path to connect two wallets; Path bookmarks save traces.
  • Compare: pin up to 3 clusters or wallets and compare flows, counterparties, and overlap.
  • Export & share: screenshot/video; CSV of visible edges/nodes; shareable link with filters encoded.

​
Visual language

  • Nodes:
    • Your wallet = gold with gentle pulse.
    • Exchanges = blue with icon; contracts = purple; smart‑money = green; whales scale larger by holdings.
    • Color heat from profitability (green → red); brightness = recency of activity.
  • Edges:
    • Solid = direct transfers; dashed = DEX swaps; dotted = social/ownership link; gradient = influence path.
    • Thickness = interaction frequency/size; opacity = relationship strength; arrows = flow direction.
Tooltips and the side panel explain any metric with timestamp and method notes.

​
Use Cases & Analysis Patterns

Whale watching. Filter by whales and a token; follow thick outbound lines to see accumulation, distribution, or OTC‑like moves. Add an alert from the panel if net outflow exceeds your threshold. Smart‑money tracking. Highlight smart‑money nodes; look for tight clusters rotating into a new chain or sector. Use Money Trail to identify common destinations and copy‑pressure risk. Risk assessment. For a project treasury or team wallet, inspect counterparties and cluster concentration. Rapid peel chains or synchronized moves across fresh wallets are red flags. Market research. Map holder distribution for a token. Are hubs exchange‑centric or many small clusters? Use Influence Path to see how attention and liquidity might propagate. Ops & investigations. Paste two addresses and run Shortest Path to find links; export the subgraph as CSV for reports. Interpretation tips. Reduce clutter with filters before judging structure; follow the thickest, most recent edges first; compare two windows (e.g., last 24h vs last 7d) to separate noise from regime change.