Wallet clustering starts with a hypothesis: these addresses may be controlled by one actor or coordinated system. It should not start with a declaration of identity.

The first job is to define what you are resolving. Are you looking for wallets controlled by one person, addresses operated by one service, or an organized activity pattern? Those are different targets. Then normalize chain events, generate candidate links and score the evidence, including reasons the link might be false.

Shared funding alone is weak when the source is an exchange or another service used by many unrelated people. Similar timing can reflect an airdrop or market event. Repeated interaction with a popular contract is not proof of shared control. Cross-check direct transactions, operational context, first-party disclosures and counterexamples before promoting a cluster into a high-confidence claim.

The outcome is allowed to be unresolved. Store the evidence and confidence level so that the conclusion can be challenged or revised later. That matters for research, analytics and security alike: false merging can make independent users look like one whale, while missed links can hide coordinated behavior.

TokenToolHub’s implementation guide explains ingestion, feature extraction, candidate generation, scoring and human review:

https://tokentoolhub.com/entity-resolution-for-wallets/

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