You are working on a social product with these tables: ### Tables / Schemas **users** - `user_id` BIGINT (PK) - `created_at` TIMESTAMP **posts** - `post_id` BIGINT (PK) - `author_id` BIGINT (FK → users.user_id) - `created_at` TIMESTAMP **engagement** (one row per engagement event) - `engagement_id` BIGINT (PK) - `post_id` BIGINT (FK → posts.post_id) - `viewer_id` BIGINT (FK → users.user_id) - `event_type` STRING (e.g., `like`, `comment`, `share`) - `created_at` TIMESTAMP **follows** (directed edge) - `follower_id` BIGINT (FK → users.user_id) - `followee_id` BIGINT (FK → users.user_id) - `created_at` TIMESTAMP (when the follow happened) Assume timestamps are in UTC, and “month” means `DATE_TRUNC('month', created_at)`. --- ## 1) Influence score Define a user’s **influence score** as: \[ \text{influence_score}(u) = \#\text{engagement events received on u's posts} + \#\text{followers of u} \] Write a SQL query returning: - `user_id` - `influence_score` Notes: - Engagement “received” means engagement rows on posts where `posts.author_id = user_id`. - Followers count is the number of distinct `follower_id` where `followee_id = user_id`. --- ## 2) Monthly follower growth For each user (as the followee), compute monthly follower growth metrics. Write a SQL query that outputs, for each `followee_id` and month: - `month` - `followee_id` - `new_followers` (distinct new followers gained that month) - `prev_month_new_followers` (previous month’s `new_followers` using `LAG`) - `mom_growth` (month-over-month growth rate: `(new_followers - prev_month_new_followers) / prev_month_new_followers`, handling divide-by-zero) - `cumulative_followers` (running total across months using a window `SUM`) Assume unfollows are not tracked (so follower counts only increase).