PageRank
The PageRank algorithm measures the influence of each vertex on every other vertex. PageRank influence is defined recursively: a vertex’s influence is based on the influence of the vertices which refer to it. A vertex’s influence tends to increase if (1) it has more referring vertices or if (2) its referring vertices have higher influence. The analogy to social influence is clear.
A common way of interpreting PageRank value is through the Random Network Surfer model. A vertex’s PageRank score is proportional to the probability that a random network surfer will be at that vertex at any given time. A vertex with a high PageRank score is a vertex that is frequently visited, assuming that vertices are visited according to the following Random Surfer scheme:

Assume a person travels or surfs across a network’s structure, moving from vertex to vertex in a long series of rounds.

The surfer can start anywhere. This startanywhere property is part of the magic of PageRank, meaning the score is a truly fundamental property of the graph structure itself.

Each round, the surfer randomly picks one of the outward connections from the surfer’s current location. The surfer repeats this random walk for a long time.

But wait. The surfer doesn’t always follow the network’s connection structure. There is a probability (1damping, to be precise), that the surfer will ignore the structure and will magically teleport to a random vertex.
For more information, see the Google paper on PageRank.
Specifications
tg_pageRank (STRING v_type, STRING e_type, FLOAT max_change=0.001, INT max_iter=25, FLOAT damping=0.85, INT top_k = 100, BOOL print_accum = TRUE, STRING result_attr = "", STRING file_path = "", BOOL display_edges = FALSE)
Characteristic  Value 

Result 
Computes a PageRank value (FLOAT type) for each vertex. 
Input Parameters 

Result Size 
V = number of vertices 
Time Complexity 
O(E*k), E = number of edges, k = number of iterations. The number of iterations is datadependent, but the user can set a maximum. Parallel processing reduces the time needed for computation. 
Graph Types 
Directed edges 
Example
# Use _ for default values
RUN QUERY tg_pageRank("Person", "Friend", 0.001, 25, 0.85, 100 _, _, _, _)
We ran pageRank on our test10 graph (using Friend edges) with the following parameter values: damping=0.85, max_change=0.001, and max_iter=25. We see that Ivy (center bottom) has the highest pageRank score (1.12). This makes sense since there are 3 neighboring persons who point to Ivy, more than for any other person. Eddie and Justin have scores of exactly 1 because they do not have any outedges. This is an artifact of our particular version pageRank. Likewise, Alex has a score of 0.15, which is (1damping), because Alex has no inedges.