diff --git a/caliGraph.py b/caliGraph.py
index ccd4bff..a2de9b4 100755
--- a/caliGraph.py
+++ b/caliGraph.py
@@ -799,7 +799,7 @@ def addImageToNode(node, cache, shape='circularImage'):
     else:
         img = cache[name]
     if img:
-        node['imagePadding'] = '100px'
+        #node['imagePadding'] = '100px'
         node['image']=img
         node['shape']=shape
 
@@ -992,7 +992,7 @@ def progress(G, minimum=3.5):
     print('Progress: '+str(perc)+'%')
 
 
-def analyze(G, type_name, name, dist=2.1):
+def analyze(G, books, type_name, name, dist=2.1):
     from fuzzywuzzy import fuzz
     type_ident = type_name[0]
     full_name = type_ident + "/" + name
@@ -1009,6 +1009,8 @@ def analyze(G, type_name, name, dist=2.1):
     if bestRatio < 70:
         print("Best Match: "+match['label'])
 
+    findNewBooks(G, books, num=-1, minRecSco=1)
+
     menge = set()
     waveFlow(G, match, n, dist, menge)
     for n in list(G.nodes):
@@ -1093,6 +1095,7 @@ def shell(G, books, mu, std):
 
 def newBooks(G, books, num, mu, std):
     removeBad(G, mu-std*2)
+    removeThinRecs(G, 2)
     findNewBooks(G, books, num, minRecSco = mu-std)
     removeUnread(G)
     removeUselessReadBooks(G)
@@ -1107,9 +1110,7 @@ def newBooks(G, books, num, mu, std):
     addScoreToLabels(G)
 
 
-def findNewBooks(G, books, num, minRecSco=5):
-    removeBad(G, 0.1, groups=['recommender'])
-    removeThinRecs(G, 2)
+def findNewBooks(G, books, num=-1, minRecSco=5):
     mrbdf = pd.read_csv('mrb_db.csv')
     recs = []
     for n in list(G.nodes):
@@ -1140,13 +1141,13 @@ def findNewBooks(G, books, num, minRecSco=5):
             scores = []
             for m in list(G.adj[n]):
                 adj = G.nodes[m]
-                if adj['t'] == 'recommender':
+                if adj['t'] == 'recommender' and adj['score']!=None:
                     scores.append(adj['score'])
                     ses.append(adj['se'])
-            ses.append(min(ses))
-            if False and len(scores) < 2:
+            if not len(scores):
                 G.remove_node(n)
             else:
+                ses.append(min(ses))
                 node['fake_se'] = sum(ses)/(len(ses)**1.2) + 0.5 + 0.5 * (len(scores)==1) # This is not how SE works. DILLIGAF?
                 node['score'] = sum(scores)/len(scores)*1.2 - node['fake_se']*2 + 0.5 - 0.1/math.sqrt(len(scores))
                 if len(scores)==1:
@@ -1154,7 +1155,8 @@ def findNewBooks(G, books, num, minRecSco=5):
                 node['value'] = 20 + 5 * float(node['score'])
                 node['label'] += " ({:.2f}±{:.1f})".format(node['score'], node['fake_se'])
                 node['label'] += '\n ' + node['author']
-    removeKeepBest(G, num, 10, 'newBook')
+    if num!=-1:
+        removeKeepBest(G, num, 10, 'newBook')
 
 # while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
 # but might be necessary to enable later for a larger libary for better training performance...
@@ -1354,7 +1356,7 @@ def cliInterface():
     elif args.cmd=="read":
         readBooksAnalysis(G, args.min_rating, args.all_tags, args.only_connected, not args.keep_top_lists)
     elif args.cmd=="analyze":
-        analyze(G, args.type, args.name, args.d)
+        analyze(G, books, args.type, args.name, args.d)
     elif args.cmd=="full":
         fullGraph(G, not args.keep_top_lists)
     elif args.cmd=="competence":