Validators process transactions and receive transactions fees (supply-side fees).

Injective is a smart contract platform for decentralized finance applications (a blockchain) and an orderbook exchange protocol. Injective was founded by Eric Chen, Albert Chon etc. and launched in 2020. Note that this dashboard only tracks Injective's blockchain business.

INJ holders own and govern the protocol. They currently take a cut of the total transaction fees paid by users (revenue) via Burn Auctions. Injective's investors include Binance, Jump Crypto, Mark Cuban, etc.

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Blockchains (L1)

Injective

# Injective Active addresses (weekly) Methodology

## 1. Overview

Weekly active addresses measures the number of unique addresses that conducted at least one transaction on the Injective protocol within a rolling 7-day window. This metric serves as a proxy for user engagement and adoption by tracking distinct transaction senders across both Injective's EVM-compatible layer and its native Cosmos-based infrastructure. The calculation uses HyperLogLog probabilistic counting to efficiently handle large datasets while maintaining accuracy for distinct address counting across the weekly rolling window.

## 2. Data sources

The calculation relies on the following blockchain data sources:

- **Injective EVM transaction data**: Standard blockchain transaction records containing transaction metadata including sender addresses (`from_address`), transaction hashes, block timestamps, and other transaction details
- **Injective Cosmos message events**: Native Cosmos SDK message events with transaction details stored as JSON attributes, specifically extracting sender information from the `message` event type 
- **Chain availability configuration**: Internal configuration data used to filter results based on data availability and quality standards for each supported chain

## 3. Step-by-step calculation

### Component A: Injective EVM user extraction
- **Purpose**: Identifies unique transaction senders from Injective's EVM-compatible layer
- **Logic**: Extracts all transaction records from Injective EVM transaction data, capturing the `from_address` field as the user address for each transaction. Each record is tagged with chain identifier 'Injective EVM', market sector 'blockchains_l1', business segment 'blockchain', and user type 'Transaction sender'. Block timestamps are safely cast to ensure proper timestamp formatting.
- **Key fields**: `block_timestamp` (transaction time), `transaction_hash` (unique transaction identifier), `from_address` (sender address), `chain` (set to 'Injective EVM')

### Component B: Injective cosmos user extraction  
- **Purpose**: Identifies unique transaction senders from Injective's native Cosmos infrastructure
- **Logic**: Processes Cosmos SDK message events, filtering for event type 'message' and extracting sender addresses from the JSON attributes using the `$.sender` path. Each record is tagged with chain identifier 'injective', market sector 'blockchains_l1', business segment 'blockchain', and user type 'Transaction sender'. This captures users interacting with Injective's native Cosmos-based functionality.
- **Key fields**: `block_timestamp` (event time), `transaction_hash` (transaction identifier), `event_attributes` (JSON containing sender information), `chain` (set to 'injective')

### Component C: Daily active user aggregation
- **Purpose**: Creates daily distinct user counts by combining EVM and Cosmos user activity
- **Logic**: Unions the EVM and Cosmos user datasets, then truncates timestamps to day boundaries and deduplicates user addresses within each day. The result provides daily snapshots of unique active addresses across both Injective infrastructures. Chain availability filters are applied to ensure data quality and completeness.
- **Key fields**: `timestamp` (day boundary), `timestamp_grain` (set to 'day'), `chain` (infrastructure type), `user_address` (unique daily user)

### Component D: Weekly active user calculation with HyperLogLog
- **Purpose**: Calculates rolling 7-day unique address counts using probabilistic data structures
- **Logic**: Creates HyperLogLog sketches for each day's unique users, then generates a datespine covering all relevant dates and creates 7-day rolling windows by joining each date with the previous 6 days. For each day in the window, merges the HLL sketches and extracts the approximate distinct count. This approach efficiently handles large datasets while maintaining accuracy for distinct counting. The calculation produces breakdowns across multiple dimensions including chain-level, project-level, user-type-level, and product-level aggregations.
- **Key fields**: `timestamp` (window end date), `user_wau` (weekly active user count), `dimensions` (breakdown category), `chain_id` (chain identifier)

### Final aggregation
- **Combining components**: Filters the weekly active user results to include only 'chain' and 'project_dbt_tag' dimension breakdowns, representing chain-level and overall project-level metrics respectively
- **Metric transformation**: Relabels the `user_wau` field as `active_addresses_weekly` to reflect the business definition of the metric as active addresses rather than active users
- **Time aggregation**: Maintains daily timestamp precision with each day representing the end of its respective 7-day rolling window
- **Final calculation**: The final weekly active addresses value represents the approximate count of unique addresses that sent at least one transaction within the trailing 7-day period, calculated using HyperLogLog probabilistic counting for computational efficiency

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

Unique active addresses over a rolling 7-day window.

InjectiveINJ

Active addresses (weekly)

Active addresses (weekly)

Competitive landscape

Chain breakdown