Network participants process transactions and receive fees from users (supply-side fees).

Cosmos Hub is an interoperability & security platform for blockchains and applications. Cosmos was founded by Ethan Buchman & Jae Kwon and launched in 2019.

ATOM holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue). Cosmos’s investors include Paradigm, IOSG Ventures, 1confirmation, etc.

governance

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Blockchains (L1)

Cosmos Hub

# Cosmos Hub Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for Cosmos measures the number of unique users who performed at least one transaction on the Cosmos blockchain within a rolling 7-day period. This metric answers the business question: "How many distinct users are actively engaging with the Cosmos network on a weekly basis?" The calculation uses HyperLogLog data structures to efficiently estimate unique user counts across time windows while avoiding double-counting users who are active on multiple days within the same week.

## 2. Data sources

- **Cosmos blockchain events**: Message events from the Cosmos Hub blockchain containing transaction details, timestamps, and user interactions
- **Transaction metadata**: Block timestamps for temporal aggregation and event type classification
- **User identification data**: Sender addresses extracted from JSON event attributes in blockchain message events

## 3. Step-by-step calculation

### Component A: User address extraction
- **Purpose**: Identifies unique user addresses from Cosmos blockchain transactions
- **Logic**: Extracts transaction data from Cosmos Hub message events where the event type equals 'message'. User addresses are obtained by parsing the `sender` field from JSON-formatted event attributes. Each transaction is classified with metadata including chain identifier ('cosmos'), market sector ('blockchains_l1'), business segment ('blockchain'), and user type ('Transaction sender'). Block timestamps are preserved for temporal analysis.
- **Key fields**: `block_timestamp`, `transaction_hash`, `user_address` (extracted from sender field), `chain`, `user_type`

### Component B: Daily active user aggregation
- **Purpose**: Creates daily snapshots of unique active users using HyperLogLog sketches
- **Logic**: Groups user addresses by day using timestamp truncation to day boundaries. Applies distinct user counting within each day, chain, user type, and product combination. Initializes HyperLogLog sketches with precision parameter 15 for efficient approximate counting of unique users. This approach enables scalable aggregation across large datasets while maintaining accuracy for cardinality estimation.
- **Key fields**: `timestamp` (truncated to day), `timestamp_grain` (set to 'day'), `chain_id`, `user_type`, `product_id`, `user_dau` (unique user address), HyperLogLog sketch (`hll`)

### Component C: Date spine and rolling window creation
- **Purpose**: Generates complete time series and creates 7-day rolling windows for WAU calculation
- **Logic**: Creates a continuous date spine from the minimum date in the DAU data to the current date minus one day, ensuring no missing days in the time series. For each date in the spine, generates a 7-day window by creating timestamps for the current day and the previous 6 days. This windowing approach captures all users active within any 7-day period ending on each specific date.
- **Key fields**: `timestamp` (target date), `joinday` (each day within the 7-day window)

### Component D: Weekly active user window aggregation
- **Purpose**: Combines daily HyperLogLog sketches across 7-day windows to calculate weekly unique users
- **Logic**: Joins the 7-day windows with daily HyperLogLog sketches, matching each window day with its corresponding daily user data. This creates a dataset where each target date has associated user sketches from its 7-day lookback period. The result preserves all dimensional breakdowns while preparing data for unique user counting across the weekly window.
- **Key fields**: `timestamp` (target date), `chain_id`, `user_type`, `product_id`, `hll` (daily HyperLogLog sketch)

### Component E: Dimensional breakdowns
- **Purpose**: Calculates WAU across different analytical dimensions
- **Logic**: Creates four separate dimensional views by merging HyperLogLog sketches and extracting final unique user counts. Project-level breakdown aggregates all chains and dimensions. Chain-level breakdown groups by individual chains. User-type breakdown groups by transaction sender categories. Product-level breakdown groups by specific products. Each breakdown nullifies unused dimension fields and extracts the final unique user count from merged HyperLogLog sketches.
- **Key fields**: `dimensions` (breakdown type), `chain_id`, `user_type`, `product_id`, `user_wau` (weekly active user count)

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a comprehensive dataset with multiple analytical perspectives in a single result set
- **Time aggregation**: Daily boundary logic based on timestamp truncation, with 7-day rolling windows ending on each date
- **Final calculation**: Weekly active users represent the unique count of user addresses extracted from merged HyperLogLog sketches across 7-day periods, providing approximate but highly accurate cardinality estimates for large-scale user activity measurement

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

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

Cosmos HubATOM

Active users (weekly)

Active users (weekly)

Competitive landscape