fix a hodgepodge of things

graphs/graph_traversals.md

@@ -73,6 +73,8 @@ def breadth_first_search(graph, start):
 
 **Example interview question using BFS:**
 
+* [Clone an undirected graph](https://www.geeksforgeeks.org/clone-an-undirected-graph/) 
+
 
 **Runtime**: O(V + E)
 

graphs/top_sort.md → graphs/topological_sort.md

@@ -1,4 +1,4 @@
-## Introduction
+## Introduction to Topological Sorting
 Aside from DFS and BFS, the most common graph concept that interviews will test is topological sorting. Topological sorting produces a linear ordering of nodes in a directed graph such that the direction of edges is respected. 
 
 A **topological sort** is an ordering of nodes for a directed acyclic graph (DAG) such that for every directed edge _uv_ from vertex _u_ to vertex _v_, _u_ comes before _v_ in the ordering.

hash_tables/hash_tables.md

@@ -11,7 +11,8 @@ address_book.get("Bob")
 '111-222-3333'
 ```
 
-Hash tables are very efficient â“ their insertion, deletion, and get operations take, on average, constant time.
+Hash tables are very efficient.  Operations such as insertion, deletion, and get take, on average, constant time.
+
 ## How it works:
 ### Hash Codes
 Internally,a hash table stores its values in an array. A special **hash function** is utilized to convert each key into a code, which is then converted into an index into the underlying array. This hash function has a hard requirement to return the same hash code for equal keys.

strings_arrays/binary_search.md

@@ -1,3 +1,5 @@
+### Introduction
+
 Binary search is a technique for efficiently locating an element in a sorted list. Searching for an element can done naively in **O(n)** time by checking every element in the list, but binary search's optimization speeds it up to **O(log n)**. Binary search is a great tool to keep in mind for array problems.
 
 Algorithm