postProcessDensOpt: Density Optimization
In DynComm: Dynamic Network Communities Detection and Generation

Description Usage Arguments Details Value Performance Methods Author(s) Examples

Implementation of the density optimization algorithm as a post processing algorithm.

1	postProcessDensOpt(dyncomm,Parameters)

`dyncomm`	a DynCom post processing algorithm. See `DynCommPostProcess`
`Parameters`	A two column matrix defining the parameters for this algorithm. See the PARAMETERS section on this page

Is an algorithm that provides a community structure, not explicitly based on modularity, but based on the increase of the average community density. Contrary to modularity-based algorithms, it tends to disband large communities into smaller ones. It is an algorithm currently developed for directed networks only.

postProcessDensOpt object

Initialization: Uses a matrix with three columns and a maximum of verticelAll()^2 rows with the edges between vertices and their weight (vertex<->vertex<->weight) of the original graph. Temporarily stores a copy of the graph to calculate a new community mapping.
Results: Uses a matrix with two columns and verticesAll() rows with the new community mapping (vertex<->community). Uses a matrix with three columns and a maximum of communityCount()^2+communityCount() rows with the edges between communities and their weight (community<->community<->weight).

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
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

## Not run: 
Parameters<-matrix(c("-e","0.1"),1,2,TRUE)
dc<-DynCommMain(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)