DynCommPostProcessR: DynCommPostProcessR(postProcessing, previous, Parameters)
In DynComm: Dynamic Network Communities Detection and Generation

Description Usage Arguments Details Value PARAMETERS Methods Author(s) See Also Examples

Provides a single interface for all post processing algorithms implemented in R.

1	DynCommPostProcessR(postProcessing, previous, Parameters)

`Parameters`	A two column matrix defining additional parameters. See the PARAMETERS section on this page
`Algorithm`	One of the available ALGORITHM See `ALGORITHM`
`Criterion`	One of the available CRITERION. See `CRITERION`

Includes methods to get results of processing and to interact with the vertices, edges and communities.

DynCommPostProcessR object

A two column matrix defining additional parameters to be passed to the selected ALGORITHM and CRITERION. The first column names the parameter and the second defines its value.

-c: Owsinski-Zadrozny quality function parameter. Values [0.0:1.0]. Default: 0.5
-k: Shi-Malik quality function kappa_min value. Value > 0 . Default 1
-w: Treat graph as weighted. In other words, do not ignore weights for edges that define them when inserting edges in the graph. A weight of exactly zero removes the edge instead of inserting so its weight is never ignored. Without this parameter defined or for edges that do not have a weight defined, edges are assigned the default value of 1 (one). As an example, reading from a file may define weights (a third column) for some edges (defined in rows, one per row) and not for others. With this parameter defined, the edges that have weights that are not exactly zero, have their weight replaced by the default value.
-e: Stops when, on a cycle of the algorithm, the quality is increased by less than the value given in this parameter.
cv: Community-Vertex. Boolean parameter that indicates if sending community mapping to a file prints the community first, if true, or the vertex first, if false. See communityMapping for details. Default TRUE

results(differential): Get additional results of the algorithm or the currently selected post processing steps. See results
quality(): Get the quality measurement of the graph after the last iteration. See quality
communityCount(): Get the number of communities after the last iteration. See communityCount
communities(): Get all communities after the last iteration. See communities
communitiesEdgeCount(): Get the number of community to community edges in the graph. See communitiesEdgeCount
communityNeighbours(community): Get the neighbours of the given community after the last iteration. See communityNeighbours
communityInnerEdgesWeight(community): Get the sum of weights of the inner edges of the given community after the last iteration. See communityInnerEdgesWeight
communityTotalWeight(community): Get the sum of weights of all edges of the given community after the last iteration. See communityTotalWeight
communityEdgeWeight(source,destination): Get the weight of the edge that goes from source to destination after the last iteration. See communityEdgeWeight
communityVertexCount(community): Get the amount of vertices in the given community after the last iteration. See communityVertexCount
community(vertex): Get the community of the given vertex after the last iteration. See community
vertexCount(): Get the total number of vertices after the last iteration. See vertexCount
verticesAll(): Get all vertices in the graph after the last iteration. See verticesAll
neighbours(vertex): Get the neighbours of the given vertex after the last iteration. See neighbours
edgeWeight(source,destination): Get the weight of the edge that goes from source vertex to destination vertex after the last iteration. See edgeWeight
vertices(community): Get all vertices belonging to the given community after the last iteration. See vertices
edgeCount(): Get the number of vertex to vertex edges in the graph. See edgeCount
communityMapping(): Get the community mapping for all communities after the last iteration.See communityMapping
communityMapping(): Get the community mapping for all communities after the last iteration.See communityMapping

poltergeist0

DynComm

## Not run: 
Parameters<-matrix(c("-e","0.1"),1,2,TRUE)
dc<-DynComm(ALGORITHM$LOUVAIN,CRITERION$MODULARITY,Parameters)
dc$addRemoveEdgesFile("initial_graph.txt")
dc$communityCount()
dc$communities()
dc$communityNodeCount(1)
dc$vertices(1)
dc$communityMapping(TRUE)
dc$time()
dc$addRemoveEdgesFile("s0000000000.txt")

## End(Not run)