Collators aggregate parachain transactions block candidates and producing state transition proofs for validators based on those blocks.

Unique Network is a blockchain specialized for NFTs powered by Polkadot Relay chain.

UNQ holders own and govern the protocol. Unique Network’s investors include Outlier Ventures, Animoca Brands, etc.

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Unique Network

# Unique Network Active addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique wallet addresses that initiated transactions on Unique Network within a rolling 30-day window. This metric captures user engagement across both the native Substrate-based Unique Network and its EVM-compatible layer. The calculation uses HyperLogLog approximation for efficient computation of unique address counts over time.

## 2. Data sources

The metric relies on the following raw blockchain data sources:

- **Native blockchain transactions**: Unique Network extrinsic records containing transaction metadata and sender addresses
- **EVM transaction events**: Unique Network blockchain event logs from the ethereum and evm pallets, specifically filtering for `Executed` method events that contain EVM transaction data
- **Transaction metadata**: Block timestamps and transaction identifiers for temporal aggregation

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identify unique user addresses from both native and EVM transaction types
- **Logic**: Extract user addresses from two distinct sources. For native Unique Network transactions, the sender address is directly available in the `id` field of extrinsic records. For EVM-compatible transactions, addresses are extracted from event logs where the `event_pallet` is ethereum or evm and the `event_method` is `Executed`, with the user address parsed from the first element of the `event_data` JSON array
- **Key fields**: User addresses are standardized as strings, block timestamps are converted to timestamp format, and transactions are classified by chain type (uniquenetwork or Unique Network EVM)

### Component B: Daily active user aggregation  
- **Purpose**: Calculate daily unique active addresses across both chain types
- **Logic**: Group transactions by day using timestamp truncation to daily granularity. For each day, count distinct user addresses that appear in any transaction. Each unique address is counted only once per day regardless of transaction frequency. All users are classified as transaction senders
- **Key fields**: Daily timestamps (`timestamp`), unique user addresses (`user_dau`), chain identifiers, and user type classification

### Component C: HyperLogLog sketch generation
- **Purpose**: Create efficient data structures for approximate unique counting over rolling windows  
- **Logic**: Generate HyperLogLog sketches for each day's unique user addresses. HyperLogLog provides memory-efficient approximate counting of distinct elements, essential for calculating rolling monthly unique counts without storing all individual addresses
- **Key fields**: Daily HLL sketches (`hll`) containing compressed representations of unique address sets, grouped by timestamp, chain, user type, and product dimensions

### Component D: Rolling window calculation
- **Purpose**: Calculate 30-day rolling unique address counts
- **Logic**: Create a complete date spine covering the analysis period. For each date, generate a 30-day lookback window (29 prior days plus the current day). Join the date spine with daily HLL sketches to ensure all days are represented in each rolling window. Merge HLL sketches within each 30-day window to calculate approximate unique address counts
- **Key fields**: Window timestamps, merged HLL sketches representing 30-day unique address sets, and dimensional breakdown fields

### Final aggregation
- **Combining components**: HyperLogLog sketches are merged using `hll_count.merge_partial()` function across each 30-day window, then unique counts are extracted using `hll_count.extract()` function
- **Dimensional breakdowns**: Monthly active address counts are calculated across multiple dimensions including project-level (project_dbt_tag), chain-level (uniquenetwork vs Unique Network EVM), user type, and product breakdowns
- **Time aggregation**: Rolling 30-day windows are calculated for each day, providing a smoothed view of monthly engagement trends
- **Final calculation**: The final metric represents the approximate count of unique addresses that were active within any rolling 30-day period, with the `user_mau` field renamed to `active_addresses_monthly` to reflect the business meaning

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique addresses active over a rolling 30-day window.

Unique NetworkUNQ

Archived

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape

Chain breakdown