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<h2 title="Class PageRank" class="title">Class PageRank</h2>
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<li>java.lang.Object</li>
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<pre>public class <span class="strong">PageRank</span>
extends java.lang.Object
implements <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a></pre>
<div class="block">PageRank algorithm implementation. There are two implementations of PageRank implemented.
<p>
The first implementation uses the standalone <code>Graph</code> interface and runs PageRank
for a fixed number of iterations:
<pre><code>
var PR = Array.fill(n)( 1.0 )
val oldPR = Array.fill(n)( 1.0 )
for( iter <- 0 until numIter ) {
swap(oldPR, PR)
for( i <- 0 until n ) {
PR[i] = alpha + (1 - alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum
}
}
</code></pre>
<p>
The second implementation uses the <code>Pregel</code> interface and runs PageRank until
convergence:
<p>
<pre><code>
var PR = Array.fill(n)( 1.0 )
val oldPR = Array.fill(n)( 0.0 )
while( max(abs(PR - oldPr)) > tol ) {
swap(oldPR, PR)
for( i <- 0 until n if abs(PR[i] - oldPR[i]) > tol ) {
PR[i] = alpha + (1 - \alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum
}
}
</code></pre>
<p>
<code>alpha</code> is the random reset probability (typically 0.15), <code>inNbrs[i]</code> is the set of
neighbors whick link to <code>i</code> and <code>outDeg[j]</code> is the out degree of vertex <code>j</code>.
<p>
Note that this is not the "normalized" PageRank and as a consequence pages that have no
inlinks will have a PageRank of alpha.</div>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/graphx/lib/PageRank.html#PageRank()">PageRank</a></strong>()</code> </td>
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<td class="colFirst"><code>static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/graphx/lib/PageRank.html#run(org.apache.spark.graphx.Graph, int, double, scala.reflect.ClassTag, scala.reflect.ClassTag)">run</a></strong>(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
int numIter,
double resetProb,
scala.reflect.ClassTag<VD> evidence$1,
scala.reflect.ClassTag<ED> evidence$2)</code>
<div class="block">Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/graphx/lib/PageRank.html#runUntilConvergence(org.apache.spark.graphx.Graph, double, double, scala.reflect.ClassTag, scala.reflect.ClassTag)">runUntilConvergence</a></strong>(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
double tol,
double resetProb,
scala.reflect.ClassTag<VD> evidence$5,
scala.reflect.ClassTag<ED> evidence$6)</code>
<div class="block">Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.</div>
</td>
</tr>
<tr class="altColor">
<td class="colFirst"><code>static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/graphx/lib/PageRank.html#runUntilConvergenceWithOptions(org.apache.spark.graphx.Graph, double, double, scala.Option, scala.reflect.ClassTag, scala.reflect.ClassTag)">runUntilConvergenceWithOptions</a></strong>(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
double tol,
double resetProb,
scala.Option<java.lang.Object> srcId,
scala.reflect.ClassTag<VD> evidence$7,
scala.reflect.ClassTag<ED> evidence$8)</code>
<div class="block">Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.</div>
</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object></code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/graphx/lib/PageRank.html#runWithOptions(org.apache.spark.graphx.Graph, int, double, scala.Option, scala.reflect.ClassTag, scala.reflect.ClassTag)">runWithOptions</a></strong>(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
int numIter,
double resetProb,
scala.Option<java.lang.Object> srcId,
scala.reflect.ClassTag<VD> evidence$3,
scala.reflect.ClassTag<ED> evidence$4)</code>
<div class="block">Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.</div>
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<h4>PageRank</h4>
<pre>public PageRank()</pre>
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<pre>public static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object> run(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
int numIter,
double resetProb,
scala.reflect.ClassTag<VD> evidence$1,
scala.reflect.ClassTag<ED> evidence$2)</pre>
<div class="block">Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>graph</code> - the graph on which to compute PageRank</dd><dd><code>numIter</code> - the number of iterations of PageRank to run</dd><dd><code>resetProb</code> - the random reset probability (alpha)
<p></dd><dd><code>evidence$1</code> - (undocumented)</dd><dd><code>evidence$2</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>the graph containing with each vertex containing the PageRank and each edge
containing the normalized weight.</dd></dl>
</li>
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<pre>public static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object> runWithOptions(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
int numIter,
double resetProb,
scala.Option<java.lang.Object> srcId,
scala.reflect.ClassTag<VD> evidence$3,
scala.reflect.ClassTag<ED> evidence$4)</pre>
<div class="block">Run PageRank for a fixed number of iterations returning a graph
with vertex attributes containing the PageRank and edge
attributes the normalized edge weight.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>graph</code> - the graph on which to compute PageRank</dd><dd><code>numIter</code> - the number of iterations of PageRank to run</dd><dd><code>resetProb</code> - the random reset probability (alpha)</dd><dd><code>srcId</code> - the source vertex for a Personalized Page Rank (optional)
<p></dd><dd><code>evidence$3</code> - (undocumented)</dd><dd><code>evidence$4</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>the graph containing with each vertex containing the PageRank and each edge
containing the normalized weight.
<p></dd></dl>
</li>
</ul>
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<pre>public static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object> runUntilConvergence(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
double tol,
double resetProb,
scala.reflect.ClassTag<VD> evidence$5,
scala.reflect.ClassTag<ED> evidence$6)</pre>
<div class="block">Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>graph</code> - the graph on which to compute PageRank</dd><dd><code>tol</code> - the tolerance allowed at convergence (smaller => more accurate).</dd><dd><code>resetProb</code> - the random reset probability (alpha)
<p></dd><dd><code>evidence$5</code> - (undocumented)</dd><dd><code>evidence$6</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>the graph containing with each vertex containing the PageRank and each edge
containing the normalized weight.</dd></dl>
</li>
</ul>
<a name="runUntilConvergenceWithOptions(org.apache.spark.graphx.Graph, double, double, scala.Option, scala.reflect.ClassTag, scala.reflect.ClassTag)">
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<pre>public static <VD,ED> <a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><java.lang.Object,java.lang.Object> runUntilConvergenceWithOptions(<a href="../../../../../org/apache/spark/graphx/Graph.html" title="class in org.apache.spark.graphx">Graph</a><VD,ED> graph,
double tol,
double resetProb,
scala.Option<java.lang.Object> srcId,
scala.reflect.ClassTag<VD> evidence$7,
scala.reflect.ClassTag<ED> evidence$8)</pre>
<div class="block">Run a dynamic version of PageRank returning a graph with vertex attributes containing the
PageRank and edge attributes containing the normalized edge weight.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>graph</code> - the graph on which to compute PageRank</dd><dd><code>tol</code> - the tolerance allowed at convergence (smaller => more accurate).</dd><dd><code>resetProb</code> - the random reset probability (alpha)</dd><dd><code>srcId</code> - the source vertex for a Personalized Page Rank (optional)
<p></dd><dd><code>evidence$7</code> - (undocumented)</dd><dd><code>evidence$8</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>the graph containing with each vertex containing the PageRank and each edge
containing the normalized weight.</dd></dl>
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