Is NEAR mainnet usage broadening? A snapshot-based investigation

This report tests a hypothesis, it does not assume one: NEAR mainnet usage is broadening when transaction counts, distinct transaction signers and contract function-call activity are considered together. It uses two historical snapshots covering 2026-08-06 to 2026-11-04. This is not live data.

Result: mixed. In the latest 7 days, average daily distinct signers changed +7.1% and transactions +3.4% against the previous 7 days, but that rise depends on the last two days of the window. In the latest 30 days, signers changed -13.4% and transactions -17.7% against the previous 30 days. Function-call activity was highly concentrated on three days in October. On this evidence the hypothesis is neither confirmed nor rejected.

NEAR mainnet usage is broadening when transaction counts, distinct transaction signers, and contract function-call activity are considered together.

This is a hypothesis to investigate, not a conclusion that must be proven.

• Latest week: average daily distinct signers rose 7.1% (51,827 to 55,529), faster than transactions (+3.4%), comparing 2026-10-29 to 2026-11-04 with 2026-10-22 to 2026-10-28. This rise depends on the last two days of the window; see Data limitations.
• Distinct receiver contracts with at least one function-call action were between 832 and 921 per day from 2026-08-06 to 2026-08-13, and between 1137 and 1523 per day from 2026-10-16 to 2026-10-31. The reason for the rise is not verified.
• Transactions and distinct signers both rose in the latest week (+3.4% and +7.1%), but both rises depend on 2026-11-03 and 2026-11-04. Without those two days, the other 5 days averaged 574,975 transactions per day against 577,201 in the previous 7 days.

• Latest 30 days: average daily transactions fell 17.7% (832,538 to 684,948) and average daily distinct signers fell 13.4% (71,203 to 61,647).
• The latest 7-day averages are below the 91-day medians: signers 55,529 against a median of 70,014, transactions 596,965 against a median of 691,420.
• Concentration: on three days the top 5 receiver accounts took most function-call actions, and distinct receiver contracts did not rise on those days. 2026-10-10: 7,455,968 actions, top 5 receivers held 69.5%, 1,477 distinct receiver contracts; 2026-10-11: 12,700,267 actions, top 5 receivers held 87.4%, 1,524 distinct receiver contracts; 2026-10-15: 11,615,182 actions, top 5 receivers held 91.4%, 1,476 distinct receiver contracts. Snapshot medians: 1,482,979 actions per day, top-5 share 47.2%.
• On each of those three days one receiver account took the largest share: 2026-10-10: cooldogsclub.near held 30.5% (2,277,161 of 7,455,968 actions); 2026-10-11: cooldogsclub.near held 67.3% (8,547,546 of 12,700,267 actions); 2026-10-15: embr.playember_reserve.near held 85.2% (9,894,354 of 11,615,182 actions). The top-5 share was therefore driven mostly by one account per day. What these accounts are, and why they received so many calls, was not investigated.
• Concentration did not fall outside the spikes: the median daily top-5 receiver share was 47.2% in the first 30 days of the snapshot and 45.2% in the last 30 days, and 46.8% across all days except the three spike days. Over the same periods the median number of distinct receiver contracts per day went from 912 to 1,449. More receivers did not translate into a clearly lower top-5 share, and the reason for the rise in receivers is not verified.
• Distinct signers were unusually high on several days in early October, for example 2026-10-03 (176,719), 2026-10-04 (177,196), 2026-10-07 (175,774), against a snapshot median of 70,014. The cause is not verified, so it is unknown whether this reflects broader participation.

• Transactions: Number of rows in the transactions table for that block_date.
• Distinct signers: Number of distinct signer_account_id values among that day's transactions. This is NOT all active accounts and NOT unique users.
• Function-call actions: Number of rows in receipt_actions with action_kind = FUNCTION_CALL for that block_date. One transaction can produce several actions, so this is NOT a transaction count.
• Distinct receiver contracts: Number of distinct receipt_receiver_account_id values with at least one FUNCTION_CALL action that day. This is NOT a complete measure of active contracts.
• Top-5 calls: Function-call actions received by the 5 most-called receiver accounts that day.
• 7-day and 30-day comparisons: the average of the most recent N days against the average of the N days immediately before them. Windows end on 2026-11-04.

• Historical snapshots, not live data. Transactions and signers: snapshot taken 2026-11-05. Function calls: snapshot taken 2026-11-06.
• Only 91 days are covered. A longer history could change the picture.
• The latest 7-day window includes 2026-11-03 and 2026-11-04, when distinct signers jumped (48,060 on 11-02, 72,343 on 11-03, 79,164 on 11-04). The other 5 days of that window averaged 47,440 signers per day, against 51,827 in the previous 7 days. The cause of the jump is not verified, so the +7.1% change should be read with caution.
• Distinct signers is not unique users and not all active accounts. One person can control many accounts, and one account can be an automated program.
• Function-call actions count actions, not transactions. One transaction can create several actions, so the two counts are not directly comparable.
• The causes of the October signer elevation and of the 10, 11 and 15 October function-call spikes were not investigated. Nothing here explains them.
• Window comparisons depend on the chosen dates. The 30-day windows contain the unexplained high days, which can move the averages.
• The final day of the snapshot is 2026-11-04. Later days were excluded because a day in progress is incomplete.
• This report makes no statement about token price.

• Evidence that the October signer elevation and the October function-call spikes came from a small number of automated accounts or one application would weaken the broadening reading for those periods.
• A longer history in which signers, transactions and distinct receiver contracts rise together over several months would strengthen the hypothesis.
• Top-5 receiver share staying near the snapshot median while total actions grow would support broadening. Repeated spikes dominated by a few receivers would weaken it.
• Measuring how many signers are new or returning, which this snapshot cannot do, would test participation directly.

• NEAR public dataset in Google BigQuery: bigquery-public-data.crypto_near_mainnet_us (documentation: https://docs.near.org/data-infrastructure/big-query).
• Tables used: bigquery-public-data.crypto_near_mainnet_us.transactions and bigquery-public-data.crypto_near_mainnet_us.receipt_actions.
• SQL: sql/daily_metrics.sql, sql/function_calls_daily.sql and sql/spike_days_top5.sql. See the Data Explorer page to reproduce.