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

Gravity Alpha is an L2 smart contract platform for decentralized applications on Ethereum. Gravity Alpha was founded by Galxe and launched in 2024.

G tokenholders own and govern the protocol. Galxe's investors include Arbitrum Foundation, Multicoin Capital, Dragonfly Capital, etc.

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Blockchains (L2)

Gravity Alpha Mainnet

# Gravity Alpha Mainnet Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) measures the number of unique users who performed at least one transaction on the GravityAlpha blockchain within a rolling 7-day window. This metric provides insight into the platform's user engagement and activity levels over time. The calculation uses HyperLogLog probabilistic counting for efficient computation of unique users across multiple time periods and dimensional breakdowns.

## 2. Data sources

- **GravityAlpha blockchain transactions**: Raw transaction records from the GravityAlpha blockchain containing transaction hashes, sender addresses, receiver addresses, block timestamps, gas usage, and gas pricing information
- **Chain metadata**: GravityAlpha chain configuration including the native G token contract address (`0xbb859e225ac8fb6be1c7e38d87b767e95fef0ebd`) and chain categorization as a Layer 2 blockchain
- **Chain availability data**: Configuration data that identifies operational periods for the GravityAlpha chain to filter out periods when the chain was not available

## 3. Step-by-step calculation

### Component A: Daily active user extraction
- **Purpose**: Identifies unique daily active users from transaction activity
- **Logic**: Extracts all transaction sender addresses from GravityAlpha blockchain transactions and aggregates them by day. Each unique sender address on a given day represents one daily active user. Users are classified as "Transaction sender" type since activity is defined as sending transactions. The process truncates block timestamps to day granularity and applies incremental processing filters to handle only recent data changes.
- **Key fields**: `from_address` becomes `user_dau`, `block_timestamp` truncated to day becomes `timestamp`, chain identifier set to 'gravityalpha', and user type classified as 'Transaction sender'

### Component B: HyperLogLog sketch creation
- **Purpose**: Converts daily user counts into probabilistic data structures for efficient unique counting
- **Logic**: Transforms the daily active user addresses into HyperLogLog sketches using BigQuery's `hll_count.init()` function with precision parameter 15. This probabilistic approach allows for efficient storage and computation of unique user counts across multiple time periods without storing individual addresses. Each day's users are represented as a single HLL sketch grouped by timestamp, chain, user type, and product dimensions.
- **Key fields**: `user_dau` addresses converted to `hll` sketch, maintaining `timestamp`, `chain_id`, `user_type`, and `product_id` grouping dimensions

### Component C: Date spine and rolling window creation
- **Purpose**: Ensures complete time series coverage and creates 7-day rolling windows for each day
- **Logic**: Generates a complete date spine from the earliest transaction date to the current date using `generate_timestamp_array()` with 1-day intervals. For each day in the spine, creates associations with the previous 6 days to form 7-day windows. This approach ensures that missing days (days with zero activity) are properly handled and that each day has a complete 7-day lookback period for WAU calculation.
- **Key fields**: `timestamp` represents each calendar day, `joinday` represents each day within the 7-day window for joins with daily HLL data

### Component D: Weekly unique user aggregation
- **Purpose**: Combines daily HLL sketches within each 7-day window to count unique weekly active users
- **Logic**: Joins the 7-day window data with daily HLL sketches to collect all HLL sketches within each 7-day period. For each target day, merges HLL sketches from that day and the previous 6 days using `hll_count.merge_partial()` to combine the probabilistic counts. Extracts the final unique count using `hll_count.extract()` to produce the weekly active user count for each day.
- **Key fields**: Merged `hll` sketches produce `user_wau` counts, maintaining dimensional attributes like `chain_id`, `user_type`, and `product_id`

### Component E: Dimensional breakdowns
- **Purpose**: Creates multiple views of WAU data across different analytical dimensions
- **Logic**: Generates four separate dimensional aggregations from the same base weekly data: project-level (entire protocol), chain-level (by blockchain), user-type-level (by user classification), and product-level (by product). Each breakdown groups and re-aggregates the HLL sketches according to the specific dimension while nullifying non-relevant dimensional fields. All breakdowns are then unioned together to create a comprehensive dimensional dataset.
- **Key fields**: `dimensions` field indicates the breakdown type ('project_dbt_tag', 'chain', 'user_type', 'product_id'), with corresponding dimensional fields populated or nullified as appropriate

### Final aggregation
- **Combining components**: All dimensional breakdowns are combined using UNION ALL operations to create a single comprehensive dataset containing WAU metrics across all analytical dimensions
- **Currency conversion**: No currency conversion is required as this metric counts users, not monetary values
- **Time aggregation**: The metric maintains daily granularity with each day representing the count of unique users active in the trailing 7-day period ending on that day
- **Final calculation**: The final WAU value for each day represents the unique count of users who sent at least one transaction within the 7-day window ending on that date, calculated using merged HyperLogLog sketches for probabilistic counting accuracy

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

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

Gravity Alpha MainnetG

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown