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@@ -19,8 +19,8 @@ In BSP model
 Though similar to map reduce model, BSP preserves data in memory across supersteps and helps in reasoning iterative graph algorithms.<br />
 `Pregel` is an implementation of classic BSP model by Google (PageRank) to analyze large graphs exclusively. It was followed by open source implementations - Apache’s Giraph and Hama; which were BSP models built on top of Hadoop.
 Pregel is highly scalable, fault-tolerant and can successfully represent larger complex graphs. Google claims the API becomes easy once a developer adopts “think like a vertex” mode.
-Pregel’s computation system is iterative and every iteration is called as superstep. The system takes a directed graph as input with properties assigned to both vertices and graph. At each superstep, all vertices executes in parallel, a user-defined function which represents the behavior of the vertex. The function has access to message sent to its vertex from the previous superstep S-1 and can update the state of the vertex, its edges, the graph and even send messages to other vertices which would receive in the next superstep S+1. The synchronization happens only between two supersteps.  Every vertex is either active or inactive at any superstep. The iteration stops when all the vertices are inactive. A vertex can deactivate itself by voting for it and gets active if it receives a message. This asynchronous message passing feature eliminates the shared memory, remote reads and latency of Map reduce model.
-#### Pregel’s API provides 
+Pregel’s computation system is iterative and every iteration is called as superstep. The system takes a directed graph as input with properties assigned to both vertices and graph. At each superstep, all vertices executes in parallel, a user-defined function which represents the behavior of the vertex. The function has access to message sent to its vertex from the previous superstep S-1 and can update the state of the vertex, its edges, the graph and even send messages to other vertices which would receive in the next superstep S+1. The synchronization happens only between two supersteps.  Every vertex is either active or inactive at any superstep. The iteration stops when all the vertices are inactive. A vertex can deactivate itself by voting for it and gets active if it receives a message. This asynchronous message passing feature eliminates the shared memory, remote reads and latency of Map reduce model.<br />
+Pregel’s API provides <br />
 + compute() method for the user to implement the logic to change the state of the graph/vertex at every superstep. It guarantees message delivery through an iterator at every superstep. 
 + User defined handler for handling issues like missing destination vertex etc.
 + Combiners reduce the amount of messages passed from multiple vertices to the same destination vertex.