Label Propagation
Label Propagation is a heuristic method for determining communities. The idea is simple: If the plurality of your neighbors all bear the label X, then you should label yourself as also a member of X.
The algorithm begins with each vertex having its own unique label. Then we iteratively update labels based on the neighbor influence described above. It is important that the order for updating the vertices be random. The algorithm is favored for its efficiency and simplicity, but it is not guaranteed to produce the same results every time.
In a variant version, some vertices could initially be known to belong to the same community. If they are wellconnected to one another, they are likely to preserve their common membership and influence their neighbors,
Specifications
tg_label_prop (SET<STRING> v_type, SET<STRING> e_type, INT max_iter, INT output_limit,
BOOL print_accum = TRUE, STRING file_path = "", STRING attr = "")
Time complexity
This algorithm has a complexity of \$O(E*k)\$, where \$E\$ is the number of edges and \$k\$ is the number of iterations.
Characteristic  Value 

Result 
Assigns a component id (INT) to each vertex, such that members of the same component have the same id value. 
Input Parameters 

Result Size 
V = number of vertices 
Graph Types 
Undirected edges 
Example
This is the same graph that was used in the Connected Component example. The results are different, though. The quartet of Fiona, George, Howard, and Ivy have been split into 2 groups:

(George & Ivy) each connect to (Fiona & Howard) and to one another.

(Fiona & Howard) each connect to (George & Ivy) but not to one another.
Label Propagation tries to find natural clusters and separations within connected components. That is, it looks at the quality and pattern of connections. The Connected Component algorithm simply asks the Yes or No question: Are these two vertices connected?
We set max_iter
to 10, but the algorithm reaches a steady state after 3 iterations:
# Use _ for default/empty values
RUN QUERY tg_label_prop(["Person"], ["Coworker"], 10, 1, _, _, _)