Mode Network batches users' transactions and submits them to Ethereum, paying a share of the transaction fees to Ethereum validators (expense).

Mode Network is an L2 smart contract platform for decentralized applications on Ethereum. Mode Network was founded by James Ross and launched in 2024.

Mode Network team & investors own and govern the protocol. Mode Network has received a $6 million grant from the Optimism Foundation.

github

twitter

discord

website

Blockchains (L2)

Mode Network

# Mode Network Active users (monthly) Methodology

## 1. Overview

Mode Network Monthly Active Users (MAU) measures the count of unique addresses that have sent at least one transaction on the Mode Network within a rolling 30-day window. This metric uses HyperLogLog probabilistic counting for efficient storage and computation of large-scale unique user counts. The calculation provides dimensional breakdowns by project, chain, user type, and product to enable comprehensive user analytics across different segments of the Mode Network ecosystem.

## 2. Data sources

- **Mode Network transaction records**: Complete transaction history from the Mode Network blockchain, including transaction metadata such as sender addresses, timestamps, and transaction hashes
- **Chain availability configuration data**: Data quality filters and availability windows to ensure consistent and reliable metric calculations across the Mode Network

## 3. Step-by-step calculation

### Component A: User transaction extraction
- **Purpose**: Identifies unique user addresses from Mode Network transaction activity
- **Logic**: Extracts all transaction records from Mode Network blockchain data, treating transaction sender addresses (`from_address`) as active users. Each transaction is timestamped and classified with chain metadata including market sector ('blockchains_l2'), business segment ('blockchain'), and user type ('Transaction sender'). The component filters for complete transaction records and applies data quality checks to ensure accurate user identification.
- **Key fields**: `block_timestamp` for temporal analysis, `from_address` as the unique user identifier, `transaction_hash` for transaction tracking, and classification fields like `chain`, `market_sector_id`, `business_segment_id`, `user_type`, and `product_id`

### Component B: Daily active user aggregation
- **Purpose**: Calculates daily unique active users using HyperLogLog probabilistic counting
- **Logic**: Groups transaction data by day using timestamp truncation to daily granularity. For each day, identifies distinct user addresses (`user_address`) that sent transactions and initializes HyperLogLog sketches for efficient unique counting. The component preserves dimensional attributes (chain, user type, product) for downstream analysis. Incremental processing logic ensures only new or updated data is processed during regular updates.
- **Key fields**: `timestamp` truncated to day level, `user_dau` containing unique user addresses, `timestamp_grain` set to 'day', and dimensional breakdown fields (`chain`, `user_type`, `product_id`)

### Component C: Monthly active user window creation
- **Purpose**: Constructs 30-day rolling windows for MAU calculation using datespine methodology
- **Logic**: Creates a complete date spine covering the analysis period, then generates rolling 30-day windows by joining each date with its previous 29 days using timestamp array generation. For each date in the spine, creates associations with all days in its 30-day lookback window. Joins this window structure with daily HLL user counts to create the foundation for MAU aggregation.
- **Key fields**: `timestamp` as the reference date, `joinday` representing dates within the 30-day window, and `hll` sketches containing daily unique user counts

### Final aggregation
- **Combining components**: Merges HyperLogLog sketches across the 30-day rolling windows using HLL merge operations to calculate unique users across the entire period. The probabilistic nature of HLL allows efficient combination of daily user counts while maintaining accuracy for large-scale unique counting.
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 30-day rolling windows anchored to each date, creating overlapping periods that provide smooth trend analysis. Daily boundaries are defined using timestamp truncation to ensure consistent time zone handling.
- **Final calculation**: Extracts final unique user counts from merged HLL sketches using HLL extract functions. Creates multiple dimensional views including project-level totals, chain-specific breakdowns, user type segments, and product-specific metrics. Applies finalization macros to standardize output format, fill missing periods with zeros, and ensure consistent schema across all dimensional breakdowns.

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

Unique revenue-generating users over a rolling 30-day window.

Mode NetworkMODE

Active users (monthly)

Active users (monthly)

Competitive landscape

Chain breakdown