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Blockchains (L1)

XDC Network

# XDC Network Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that initiated at least one transaction on the XDC Network within a rolling 7-day period. This metric serves as a key indicator of network adoption and user engagement for the XDC blockchain. The calculation uses HyperLogLog sketches to efficiently count distinct addresses across rolling weekly windows, ensuring accurate measurement even with high transaction volumes.

## 2. Data sources

- **XDC Network transaction data**: Complete transaction records from the XDC blockchain including transaction hashes, block timestamps, and sender addresses
- **Block timestamp data**: Precise timing information for when transactions were included in blocks, used for daily and weekly time boundaries

## 3. Step-by-step calculation

### Component A: Daily user extraction
- **Purpose**: Identify unique addresses that sent transactions each day
- **Logic**: Extract transaction records from XDC Network blockchain data, focusing on the `from_address` field which represents the transaction sender. Each transaction sender is classified as a "Transaction sender" user type. Block timestamps are truncated to day-level granularity to group transactions by calendar day. Only distinct address-day combinations are retained to avoid counting the same address multiple times within a single day.
- **Key fields**: `block_timestamp` (truncated to day), `from_address` (as `user_address`), `transaction_hash`, `chain` (set to 'xdc')

### Component B: HyperLogLog sketch creation  
- **Purpose**: Create memory-efficient data structures for counting distinct users across time windows
- **Logic**: Convert the daily distinct user lists into HyperLogLog (HLL) sketches, which are probabilistic data structures that enable efficient distinct counting across large datasets. Each day's set of unique addresses is transformed into an HLL sketch that can be merged with other sketches to count distinct addresses across multiple days without storing all individual addresses.
- **Key fields**: `timestamp` (daily), `hll` (HyperLogLog sketch), `chain_id`, `user_type`

### Component C: Rolling weekly window construction
- **Purpose**: Create 7-day rolling windows for each day to calculate weekly active addresses
- **Logic**: Build a complete datespine covering all days from the first transaction to the present day to ensure no gaps in the time series. For each day in the datespine, create a 7-day rolling window that includes the current day plus the 6 preceding days. This rolling window approach means that each day's weekly active address count represents unique addresses that were active at any point during that 7-day period.
- **Key fields**: `timestamp` (target day), `joinday` (days within the 7-day window)

### Component D: Weekly distinct user calculation
- **Purpose**: Count distinct addresses across each 7-day rolling window
- **Logic**: For each 7-day window, merge the HLL sketches from all days within that window using HLL merge operations. Extract the final distinct count from the merged sketch, which provides the number of unique addresses that were active during any day within the 7-day period. This approach efficiently handles overlapping addresses across multiple days without double-counting.
- **Key fields**: `user_wau` (weekly active user count), `timestamp`, `timestamp_grain` (set to 'day')

### Component E: Dimensional breakdowns
- **Purpose**: Create multiple views of weekly active users across different dimensions
- **Logic**: Generate separate aggregations for different analytical dimensions including project-level view (`project_dbt_tag`), chain-level view (`chain`), user type view (`user_type`), and product view (`product_id`). Each breakdown aggregates the HLL sketches according to the specified dimension while maintaining the same 7-day rolling window logic. All dimensional breakdowns are combined using union operations.
- **Key fields**: `dimensions` (breakdown category), `chain_id`, `product_id`, `user_type`

### Final aggregation
- **Combining components**: The final model takes the weekly active user data and filters to only include 'chain' and 'project_dbt_tag' dimensional breakdowns, then renames the `user_wau` field to `active_addresses_weekly` to align with the metric's naming convention
- **Time aggregation**: Weekly calculations use rolling 7-day windows updated daily, with each day representing the count of unique addresses active in the preceding 7 days including the current day
- **Final calculation**: The metric value represents the distinct count of XDC Network addresses that initiated at least one transaction within the rolling 7-day period ending on each given day

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

Unique active addresses over a rolling 7-day window.

XDC NetworkXDC

Active addresses (weekly)

Active addresses (weekly)

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