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More organizational changes and dropping the benchmark plot.
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jegonzal committed Jan 11, 2014
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Expand Up @@ -66,17 +66,7 @@ computation in one system with a single composable API. The GraphX API
enables users to view data both as a graph and as
collection (i.e., RDDs) without data movement or duplication. By
incorporating recent advances in graph-parallel systems, GraphX is able to optimize
the execution of graph operations. In preliminary experiments we find that the GraphX
system is able to achieve performance comparable to state-of-the-art
graph-parallel systems while easily expressing the entire analytics pipelines.

<p style="text-align: center;">
<img src="img/graphx_performance_comparison.png"
title="GraphX Performance Comparison"
alt="GraphX Performance Comparison"
width="50%" />
<!-- Images are downsized intentionally to improve quality on retina displays -->
</p>
the execution of graph operations.

## GraphX Replaces the Spark Bagel API

Expand Down Expand Up @@ -279,11 +269,15 @@ val outputGraph: Graph[Double, Double] =
## Structural Operators
<a name="structural_operators"></a>

## Join Operators
<a name="join_operators"></a>

## Map Reduce Triplets (mapReduceTriplets)
<a name="mrTriplets"></a>




# Graph Builders
<a name="graph_builders"></a>

Expand All @@ -295,7 +289,8 @@ val userGraph: Graph[(String, String), String]

# Optimized Representation

The Property Graph is internally represented as a collection of RDDs
This section should give some intuition about how GraphX works and how that affects the user (e.g.,
things to worry about.)

<p style="text-align: center;">
<img src="img/edge_cut_vs_vertex_cut.png"
Expand All @@ -319,6 +314,19 @@ The Property Graph is internally represented as a collection of RDDs
# Graph Algorithms
<a name="graph_algorithms"></a>

This section should describe the various algorithms and how they are used.

## PageRank

## Connected Components

## Shortest Path

## Triangle Counting

## K-Core

## LDA

<p style="text-align: center;">
<img src="img/tables_and_graphs.png"
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