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.

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Blockchains (L2)

Mode Network

# Mode Network Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Mode Network measures the number of unique addresses that sent at least one transaction within any 7-day rolling window. This metric provides insight into user engagement and activity patterns on the Mode blockchain network by tracking distinct transaction senders over weekly periods. The calculation uses HyperLogLog probabilistic counting for efficient processing of large datasets and provides multiple dimensional breakdowns for comprehensive analysis.

## 2. Data sources

- **Mode blockchain transaction data**: Raw transaction records from the Mode Network blockchain containing transaction metadata including sender addresses, timestamps, and transaction hashes
- **Token Terminal chain availability metadata**: Configuration data that defines which chains and time periods are available for analysis

## 3. Step-by-step calculation

### Component A: User identification and daily aggregation
- **Purpose**: Identifies unique users from Mode Network transactions and creates daily active user counts
- **Logic**: Extracts transaction sender addresses from Mode Network transaction records as the primary user identification method. Each transaction sender address represents a unique user. The data is filtered and standardized with Mode-specific metadata including chain identifier, market sector classification as "blockchains_l2", and business segment classification as "blockchain". All users are categorized as "Transaction sender" type since Mode Network user activity is determined by transaction sending behavior. The addresses are then aggregated to daily level by truncating transaction timestamps to day boundaries and counting distinct user addresses per day.
- **Key fields**: `block_timestamp` for timing, `from_address` as the user identifier, `transaction_hash` for transaction linking, and derived fields like `chain` identifier set to "mode"

### Component B: HyperLogLog preparation for efficient counting
- **Purpose**: Converts daily active user data into HyperLogLog sketches for efficient distinct user counting across rolling windows
- **Logic**: Takes the daily active user data and initializes HyperLogLog sketches for each day, grouped by timestamp, timestamp grain, chain, user type, and product dimensions. Uses HLL precision parameter of 15 for balance between accuracy and performance. This probabilistic data structure enables efficient merging of user sets across multiple days without storing individual user addresses.
- **Key fields**: `timestamp` for daily boundaries, `user_dau` containing the distinct user addresses, and `hll` sketches for efficient counting

### Component C: Rolling window creation with datespine
- **Purpose**: Creates a comprehensive date spine and generates 7-day rolling windows for WAU calculation
- **Logic**: Generates a complete date spine covering the full analysis period from the earliest available data to the current date minus one day. For each date in the spine, creates a 7-day lookback window by generating an array of dates from 6 days prior through the current date. This ensures every possible 7-day period is captured without gaps, even for dates with no activity. Cross-joins the datespine with the 7-day date arrays to create all possible date-window combinations needed for rolling WAU calculation.
- **Key fields**: `timestamp` for the window end date, `joinday` for each day within the 7-day window

### Component D: WAU calculation using HLL merging
- **Purpose**: Calculates weekly active users by merging HyperLogLog sketches across each 7-day window
- **Logic**: Joins the rolling window structure with the daily HLL data, matching each day in the 7-day window with its corresponding HLL sketch. Groups by the window end timestamp and dimensional attributes, then merges all HLL sketches within each 7-day period using HLL merge operations. Extracts the final distinct count from the merged HLL sketch to produce the WAU value. This approach efficiently counts unique users across each 7-day period without double-counting users who were active on multiple days within the window.
- **Key fields**: `timestamp` for the window period, dimensional grouping fields, and `hll` sketches for merging

### Component E: Dimensional breakdowns
- **Purpose**: Creates multiple aggregation levels for comprehensive WAU analysis
- **Logic**: Produces four distinct dimensional views of the WAU data. Project-level aggregation provides overall Mode Network WAU by removing all dimensional filters. Chain-level aggregation maintains chain granularity for multi-chain analysis. User-type-level aggregation segments by user classification. Product-level aggregation enables product-specific analysis. Each breakdown maintains the same rolling 7-day window logic while aggregating across the appropriate dimensions. Null values are set for non-relevant dimension fields in each breakdown.
- **Key fields**: `dimensions` field indicating the aggregation level, dimensional identifier fields, and `user_wau` containing the final count

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using union operations to create a comprehensive dataset containing WAU metrics at all aggregation levels
- **Time aggregation**: Daily timestamps represent the end date of each 7-day rolling window, with each day containing the count of unique users active within the 7 days ending on that date
- **Final calculation**: The final WAU value represents the distinct count of user addresses that sent at least one transaction within any 7-day rolling period, calculated using probabilistic HyperLogLog counting for efficiency and accuracy

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

Unique revenue-generating users over a rolling 7-day window.

Mode NetworkMODE

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown