diff --git a/PathLinker.py b/PathLinker.py
index c4a74b8..087f151 100644
--- a/PathLinker.py
+++ b/PathLinker.py
@@ -105,14 +105,7 @@ def applyEdgePenalty(net, weight):
     """
     Applies a user-specified edge penalty to each edge. This weight
     penalizes the score of every path by a factor equal to
-    (the number of edges in the path)^(this factor).
-
-    This was previously done in the logTransformEdgeWeights method
-    with a parameter weight=(sum of all edge weights). In the 
-    "standard" PathLinker case, this was necessary to account for the
-    probability that is lost when edges are removed in 
-    modifyGraphForKSP_removeEdges(), along with the probability lost
-    to zero degree nodes in the edge flux calculation.
+    (the number of edges in the path)^(weight).
 
     :param net: NetworkX graph
     :param weight: user-specified edge penalty
@@ -133,9 +126,6 @@ def logTransformEdgeWeights(net):
     converting multiplicative values (where higher is better) to
     additive costs (where lower is better).
 
-    Before the transformation, weights are normalized to sum to one,
-    supporting an interpretation as probabilities.
-
     If the weights in the input graph correspond to probabilities,
     shortest paths in the output graph are maximum-probability paths
     in the input graph.
diff --git a/README.md b/README.md
index 7dfd3ef..ec54095 100644
--- a/README.md
+++ b/README.md
@@ -45,8 +45,8 @@ method.
 
 ### What's included
   * **run.py** An end-to-end implementation of the PathLinker
-  algorithm. Given a network, a set or receptors, a set of
-  transcription factors, and a value of *k*, PathLinker outputs a ranked list of edges composing the *k* highest scoring paths connecting any receptor to any transcription factor.
+  algorithm. Given a network, a set or receptors, and a set of
+  transcription factors, run outputs a ranked list of edges encountered in the *k* highest scoring paths connecting any receptor to any transcription factor.
   * **PageRank.py** An implementation of the PageRank algorithm. This algorithm can be used as
   a component of PathLinker, but it can also be run as a standalone tool.
   Given a weighted, directed network, PageRank uses the power method to compute the
@@ -58,6 +58,10 @@ method.
   computes the *k* shortest simple paths betwen a source and target
   node. Here a path is *simple* if there are no repeated nodes in the
   path.
+  * **PathLinker.py** Given a network, a set of receptors, a set of transcription 
+  factors, and a value of *k*, PathLinker modifies the structure of the graph to 
+  calculate and output the *k* high-scoring paths from any receptor protein to any 
+  transcriptional regulator.
 
 Run any of the programs with the --help option for full documentation.
 The /example directory contains a sample usage of these programs.
diff --git a/run.py b/run.py
index 935a46f..e8ef7f2 100644
--- a/run.py
+++ b/run.py
@@ -10,7 +10,7 @@
 
 def main(args):
     usage = '''
-PathLinker.py [options] NETWORK NODE_TYPES
+run.py [options] NETWORK NODE_TYPES
 REQUIRED arguments:
     NETWORK - A tab-delimited file with one directed interaction per
         line. Each line should have at least 2 columns: tail, head. Edges
@@ -43,7 +43,7 @@ def main(args):
         help='Run PathLinker on only the largest weakly connected component of the graph. May provide performance speedup.')
         
     parser.add_option('', '--edge-penalty', type='float', default=1.0,\
-        help='Factor by which to divide every edge weight. The effect of this option is to penalize the score of every path by a factor equal to (the number of edges in the path)^(this factor). (default=1.0)')
+        help='Factor by which to divide every edge weight. The effect of this option is to penalize the score of every path by a factor equal to (the number of edges in the path)^(weight). (default=1.0)')
 
     # Random Walk Group
     group = OptionGroup(parser, 'Random Walk Options')
@@ -184,12 +184,10 @@ def main(args):
     
     ## Prepare the network to run KSP
 
-    # Remove improper edges from the sources and targets. This portion
-    # must be performed before the log transformation, so that the
-    # renormalization within accounts for the probability lost to the
-    # removed edges.  These transformations are executed by default;
-    # to prevent them, use the opts.allow_mult_sources or opts.allow_mult_targets
-    # arguments.
+    # Remove improper edges from the sources and targets. If the user runs PageRank 
+    # first, then this step will cause the total edge flux in the graph to be less than one.  
+    # These transformations are executed by default to prevent them, use the 
+    # opts.allow_mult_sources or opts.allow_mult_target arguments.
     if not opts.allow_mult_sources:
         pl.modifyGraphForKSP_removeEdgesToSources(net, sources)
     if not opts.allow_mult_targets: