anchor.diff:  occurred somewhere within a prior window.
   
   The `lag` parameter can be used to start calculating the window parameter a
  -number of ten second windows in the past. For example we could walk the graph in 20 seconds
  +number of ten second windows in the past. For example we could walk the graph in 20 second
   windows starting from 30 seconds prior to a set of root events.
   By adjusting the lag and re-running the query we can determine which lagged
   window has the highest degree. From this we can determine the delay.
  
positive.code: ----
  === Temporal Bipartite Subgraphs
  
  Once we've identified a set of root event windows it's easy to perform a graph query that creates a
  bipartite graph of the log events that occurred within the same ten second windows.
  With Solr logs there is a field called `type_s` which is the type of log event.
  
  In order to see what log events happened in the same ten second window of our root events we can "walk" the ten second windows and gather the type_s field.
  
  [source,text]
  ----
  
negative.code: The `count(+++*+++)` aggregation counts the "gathered" nodes, in this case the values in the `product_s` field.
  Notice that the `count(+++*+++)` result is the same as the number of ancestors.
  This will always be the case because the nodes function first deduplicates the edges before
  counting the gathered nodes. Because of this the `count(+++*+++)` aggregation always calculates the
  inbound degree centrality for the gathered nodes.
  
  === Dot Product
  
  There is a direct relationship between the *inbound degree* with bipartite graph recommenders and the *dot product*.
  This relationship can be clearly seen in our working example once we include a column for butter:
  
  image::images/math-expressions/graph2.png[]
  
  If we compute the dot product between the butter column and the other product columns you will find that the dot product equals the inbound degree in each case.
  This tells us that a nearest neighbor search, using a maximum inner product similarity, would select the column with the highest inbound degree.
  
  === Node Scoring
  
  The degree of the node describes how many nodes in the subgraph link to it.
  But this does not tell us if the node is particularly central to this subgraph or if it is just a
  very frequent node in the entire graph. Nodes that appear frequently in the subgraph but
  infrequently in the entire graph can be considered more *relevant* to the subgraph.
  
  The search index contains information about how frequently each node appears in the entire index.
  Using a technique similar to *tf-idf* document scoring, graph expressions can combine the
  