Update README.md (#6)

README.md

@@ -4,33 +4,10 @@ Subgraph isomorphism is a resource-heavy (but branch-parallelizable) algorithm t
 
 _Grand-Iso_ is a subgraph isomorphism algorithm that exchanges this resource-limitation for a parallelizable partial-match queue structure.
 
-## Pseudocode for "Grand-Iso" algorithm
-
-```
-- Accept a motif M, and a host graph H.
-- Create an empty list for result storage, R.
-- Create an empty queue, Q.
-- Preprocessing
-    - Identify the most "interesting" node in motif M, m1.
-    - Add to Q a set of mappings with a single node, with one map for all
-        nodes in H that satisfy the requirements of m1: degree, attributes, etc
-- Motif Search
-    - "Pop" a backbone B from Q
-        - Identify as m1 the most interesting node in motif M that does not yet
-            have a mapping assigned in B.
-        - Identify all nodes that are valid mappings from the backbone to m1,
-            based upon degree, attributes, etc.
-        - If multiple nodes are valid candidates, add each new backbone to Q.
-        - Otherwise, when all nodes in M have a valid mapping in B to H, add
-            the mapping to the results set R.
-    - Continue while there are still backbones in Q.
-- Reporting
-    - Return the set R to the user.
-```
-
 ## Example Usage
 
 ```python
+from grandiso import find_motifs
 import networkx as nx
 
 host = nx.fast_gnp_random_graph(10, 0.5)
@@ -47,6 +24,7 @@ len(find_motifs(motif, host))
 Directed graph support:
 
 ```python
+from grandiso import find_motifs
 import networkx as nx
 
 host = nx.fast_gnp_random_graph(10, 0.5, directed=True)
@@ -59,3 +37,31 @@ motif.add_edge("D", "A")
 
 len(find_motifs(motif, host))
 ```
+
+## Counts-only
+
+For very large graphs, you may use a good chunk of RAM not only on the queue of hypotheses, but also on the list of results. If all you care about is the NUMBER of results, you should pass `count_only=True` to the `find_motifs` function. This will dramatically reduce your RAM overhead on higher-count queries.
+
+## Pseudocode for "Grand-Iso" algorithm
+
+```
+- Accept a motif M, and a host graph H.
+- Create an empty list for result storage, R.
+- Create an empty queue, Q.
+- Preprocessing
+    - Identify the most "interesting" node in motif M, m1.
+    - Add to Q a set of mappings with a single node, with one map for all
+        nodes in H that satisfy the requirements of m1: degree, attributes, etc
+- Motif Search
+    - "Pop" a backbone B from Q
+        - Identify as m1 the most interesting node in motif M that does not yet
+            have a mapping assigned in B.
+        - Identify all nodes that are valid mappings from the backbone to m1,
+            based upon degree, attributes, etc.
+        - If multiple nodes are valid candidates, add each new backbone to Q.
+        - Otherwise, when all nodes in M have a valid mapping in B to H, add
+            the mapping to the results set R.
+    - Continue while there are still backbones in Q.
+- Reporting
+    - Return the set R to the user.
+```