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Risk in DeFi is rarely isolated. Protocols depend on each other’s tokens and prices; capital concentrates in a handful of wallets; a shock in one place propagates to others. Alterscope models these relationships as a knowledge graph and runs network analysis over it to surface risks that a per-asset view misses. The graph links protocols, pools, assets, and wallets through dependency, holding, and price relationships. The analyses below run on top of it.

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Contagion cascades

The cascade model answers: if this pool or asset takes a shock, what else is affected, and by how much? It propagates a shock outward across the graph’s dependency edges, dampening the impact at each hop:
Effects from multiple paths are combined as a union of independent shocks, and propagation stops once impact falls below a threshold or a maximum hop distance is reached. Alterscope runs standard scenarios (a 10%, 25%, and 50% shock) and returns the affected subgraph, which powers the contagion map in the product. Calibration. The same-chain per-hop dampening is empirically fitted against a corpus of historical contagion events (including the UST depeg, the stETH dislocation, the USDC/SVB weekend, and the Euler exploit) via grid search. The cross-chain dampening is an uncalibrated prior — the historical corpus is Ethereum-only — and is documented as such so it is read as a conservative assumption, not a fitted result. We publish the propagation formula and the calibration approach; the fitted dampening values are calibrated internally.

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Concentration

Concentration measures how much of a pool sits in how few hands — a thin, concentrated holder base is a liquidity and exit risk. Alterscope computes the Herfindahl-Hirschman Index (HHI) over holder shares, alongside top-1% / top-5% / top-10% concentration and the largest holders:
A higher HHI means more concentration. Where wallet-level holder data is unavailable for a pool, the model falls back to concentration over protocol dependency shares so a signal is still produced.

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Communities

Alterscope detects communities — clusters of protocols and assets that are densely interconnected — using the Louvain community-detection algorithm over the graph. Communities reveal which protocols effectively rise and fall together, and each community surfaces its total-value share and whether it spans multiple chains. Community assignments are precomputed on a schedule (see staleness note below) and each response reports when it was last computed.

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Smart-money flow

The smart-money analysis tracks position changes by whale and labeled entities — wallets above a size or pool-share threshold — and classifies each move as entering, exiting, or holding. The intent is to surface where sophisticated capital is rotating before it shows up in aggregate metrics.
Smart-money and wallet-level concentration depend on wallet-ingestion coverage, which varies by environment and is still expanding. Treat these as a capability whose signal quality tracks how completely the relevant wallets have been ingested — not as comprehensive coverage of all market participants. See Coverage & gaps.

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Supporting network metrics

The graph also precomputes standard network-centrality metrics as node properties, used as inputs to the analyses above and available on graph endpoints:
Batch staleness. Graph metrics and community assignments are recomputed on a schedule (faster-moving metrics hourly, heavier ones every few hours), so they can lag live state by up to their refresh interval. Endpoints report the last-computed timestamp so you can see exactly how current a result is.

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Where this shows up

Cascade, concentration, community, and smart-money results are returned by the graph endpoints in the API reference, each carrying freshness and quality metadata. The contagion / systemic signals also feed the systemic factor in the composite score. The honest boundaries — coverage dependence and the uncalibrated cross-chain prior — are on the Limitations page.