From b4b03e9a8fa57d130993a328f39c65ac7cc91202 Mon Sep 17 00:00:00 2001
From: Dylan De Faoite <dylanseandefaoite@gmail.com>
Date: Tue, 3 Feb 2026 14:07:11 +0000
Subject: [PATCH] feat: add interaction graph to user analysis

---
 server/stat_gen.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/server/stat_gen.py b/server/stat_gen.py
index ab35b03..e781580 100644
--- a/server/stat_gen.py
+++ b/server/stat_gen.py
@@ -6,6 +6,8 @@ import datetime
 from nltk.corpus import stopwords
 from collections import Counter
 
+from pprint import pprint
+
 DOMAIN_STOPWORDS = {
     "www", "https", "http",
     "boards", "boardsie",
@@ -84,6 +86,27 @@ class StatGen:
 
         return rows
     
+    def _interaction_graph(self):
+        interactions = {a: {} for a in self.df["author"].dropna().unique()}
+
+        # reply_to refers to the comment id, this allows us to map comment ids to usernames
+        id_to_author = self.df.set_index("id")["author"].to_dict()
+
+        for _, row in self.df.iterrows():
+            a = row["author"]
+            reply_id = row["reply_to"]
+
+            if pd.isna(a) or pd.isna(reply_id) or reply_id == "":
+                continue
+
+            b = id_to_author.get(reply_id)
+            if b is None or a == b:
+                continue
+
+            interactions[a][b] = interactions[a].get(b, 0) + 1
+
+        return interactions
+        
     ## Public
     def time_analysis(self) -> pd.DataFrame:
         per_day = (
@@ -219,6 +242,7 @@ class StatGen:
         return {
             "top_users": top_users,
             "users": merged_users,
+            "interaction_graph": self._interaction_graph()
         }
         
     def search(self, search_query: str) -> dict: