Added pruning of connections from authors above theshold and tweaked

some params

main.py

@@ -195,6 +195,30 @@ def pruneRecommenderCons(G, maxCons=5):
                             if foundCon < 2:
                                 G.remove_node(m)
 
+def pruneAuthorCons(G, maxCons=3):
+    for n in list(G.nodes):
+        node = G.nodes[n]
+        if node['t'] == 'author':
+            if len(G.adj[n]) > maxCons:
+                bestlist = []
+                for m in list(G.adj[n]):
+                    book = G.nodes[m]
+                    if book['t'] == 'book':
+                        if 'score' in book and book['score'] != None:
+                            bestlist.append(book)
+                bestlist.sort(key=lambda node: node['score'], reverse=True)
+                bestlist = bestlist[:maxCons]
+
+                for m in list(G.adj[n]):
+                    book = G.nodes[m]
+                    if book['t'] == 'book' and book not in bestlist or 'score' in book and book['score'] == None:
+                        if not 'rating' in book or book['rating'] == None:
+                            foundCon = 0
+                            for con in G.adj[m]:
+                                if G.nodes[con]['t'] not in ['topList']:
+                                    foundCon += 1
+                            if foundCon < 2:
+                                G.remove_node(m)
 
 def removeHighSpanTags(G, maxCons=5):
     for n in list(G.nodes):
@@ -465,7 +489,8 @@ def recommendNBooks(n):
     pruneTags(G, 4)
     removeBad(G, mu, groups=['book'])
     pruneTags(G, 3)
-    pruneRecommenderCons(G, 5)
+    pruneRecommenderCons(G, int(n/7)+1)
+    pruneAuthorCons(G, int(n/15))
     removeTopLists(G)
     removeDangling(G, alsoBooks=True)
     removeKeepBest(G, n, maxDistForRead=0.75)