Address Clustering

WHAT IT DOES Address Clustering analyzes the on-chain behavior of a given EVM wallet to identify other addresses that may be related to it. The tool examines transaction history, event logs, and contract interaction patterns within the last 5,000 blocks to build a relationship graph. It scores related addresses based on direct transactions with the target, shared counterparties, common contract interactions, and overlapping transaction patterns. Results are presented with confidence levels (high, medium, low) and detailed reasoning for each suggested relationship, such as "5 direct txns" or "3 shared counterparties." This tool is designed for investigative analytics, helping users explore potential wallet clusters, identify associated addresses, and understand the broader network around any on-chain entity. It is not a forensics tool—it surfaces heuristic connections that warrant further investigation. HOW TO USE Connect your EVM wallet or simply paste any Ethereum-compatible address into the input field. Select the network that matches the address from the dropdown menu—the tool supports Ethereum Mainnet, Sepolia, BSC, Polygon, Arbitrum, Optimism, Avalanche, and Fantom. Click the "Find clusters" button to initiate the scan. The tool will fetch transaction logs, extract transaction details, and build a graph of related addresses. A progress indicator shows the log scanning status, with automatic chunking and backoff to handle RPC limits. Results display the target address, the number of transactions analyzed, and a ranked list of related addresses with confidence badges, interaction scores, and specific reasons for each connection. Click any address to open it in a block explorer. THE TECHNICAL MECHANISM Address Clustering uses a multi-layer heuristic engine to infer relationships between addresses. The engine first collects all transactions involving the target address within a 5,000-block window, using chunked log scanning to respect RPC range limits—starting at 2,000-block chunks and halving on failure to ensure the scan completes on any provider. From these logs, it extracts transaction hashes, then fetches detailed transaction records to identify sender and receiver addresses. The core clustering algorithm scores each candidate address using a weighted system: direct transactions with the target (weight 2 per transaction), shared counterparties—addresses that have transacted with both the target and the candidate (weight 3 each), and shared contract addresses from event logs (weight 2 each). The total score determines the confidence level: high (>=10), medium (>=4), or low (