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 addresses (monthly) Methodology

## 1. Overview

Monthly active addresses measures the number of unique addresses that have initiated at least one transaction on the Mode network within a rolling 30-day period. This metric provides insight into user engagement and network activity by tracking distinct addresses that actively participate in on-chain activities. The calculation uses a rolling 30-day window approach with HyperLogLog sketches for efficient distinct address counting across overlapping time periods.

## 2. Data sources

- **Mode network transaction records**: Complete transaction data from the Mode blockchain including sender addresses, timestamps, and transaction hashes
- **Transaction metadata**: Block timestamps, transaction hashes, and sender address information for all Mode network transactions

## 3. Step-by-step calculation

### Component A: Daily active address extraction
- **Purpose**: Identifies unique addresses that initiated transactions each day on Mode network
- **Logic**: Extracts transaction sender addresses from Mode network transaction data. Each transaction's `from_address` is treated as an active address for that day. Transaction timestamps are truncated to daily boundaries to group activity by day. Each unique combination of day and sender address represents one daily active address instance.
- **Key fields**: `block_timestamp` (transaction timing), `from_address` (transaction sender address), `transaction_hash` (transaction identifier)

### Component B: HyperLogLog sketch creation
- **Purpose**: Creates efficient data structures for counting distinct addresses across time periods
- **Logic**: For each day, initializes HyperLogLog sketches containing all unique addresses that were active that day. HyperLogLog sketches enable efficient approximate distinct counting and can be merged across time periods without double-counting the same address. Each daily sketch captures the set of unique addresses active on that specific day.
- **Key fields**: `timestamp` (daily boundary), `hll` (HyperLogLog sketch of daily active addresses)

### Component C: Rolling 30-day window construction
- **Purpose**: Creates overlapping 30-day periods for calculating monthly active addresses
- **Logic**: Constructs a datespine containing consecutive days from the earliest transaction date to yesterday. For each day in the datespine, creates joins with the previous 29 days to form 30-day windows. This approach ensures every day has a complete 30-day lookback period without gaps. The window construction handles missing days by including them in the datespine even if no transactions occurred.
- **Key fields**: `timestamp` (window end date), `joinday` (each day within the 30-day window)

### Component D: Monthly active address calculation
- **Purpose**: Calculates the number of distinct addresses active within each 30-day period
- **Logic**: For each 30-day window, merges the HyperLogLog sketches from all days within that window. The merged sketch contains the distinct set of addresses that were active at least once during the 30-day period. Extracts the final count from the merged HyperLogLog sketch to get the number of distinct active addresses. This approach prevents double-counting addresses that appear on multiple days within the same 30-day window.
- **Key fields**: `user_mau` (count of distinct addresses active in 30-day window)

### Component E: Dimensional aggregation
- **Purpose**: Provides different views of the monthly active addresses metric
- **Logic**: Aggregates the monthly active addresses across multiple dimensions. Creates separate breakdowns for project-level totals, chain-specific totals, user type categories, and product-specific totals. Each dimension provides a different perspective on network activity while maintaining the same underlying 30-day distinct address counting methodology.
- **Key fields**: `dimensions` (aggregation level), `chain_id` (network identifier), `user_type` (address category), `product_id` (product identifier)

### Final aggregation
- **Combining components**: Filters the dimensional aggregations to include only 'chain' and 'project_dbt_tag' dimensions, which represent Mode network-specific and overall project-level monthly active addresses respectively
- **Currency conversion**: Not applicable - this is a count-based metric
- **Time aggregation**: Uses rolling 30-day windows updated daily, where each day represents the end point of a 30-day lookback period
- **Final calculation**: Renames the `user_mau` field to `active_addresses_monthly` to reflect that the metric represents active addresses rather than users. The value represents the approximate number of distinct addresses that initiated at least one transaction within the most recent 30-day period ending on each given day.

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

Unique addresses active over a rolling 30-day window.

Mode NetworkMODE

Active addresses (monthly)

Active addresses (monthly)

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