Description Usage Arguments Value Examples
Based on the decomposition specified by communities
object (see computeGraphClusters
)
materiazlies produced clusters as graph objects from Aster graph tables.
1 2 |
channel |
connection object as returned by |
communities |
community object returned by |
ids |
integer vector with cluster integer ids (from |
componentids |
character vector with cluster component ids assigned during community
generation with |
allTables |
pre-built information about existing tables. |
test |
logical: if TRUE show what would be done, only (similar to parameter |
parallel |
logical: enable parallel calls to Aster database. This option requires parallel
backend enabled and registered (see in examples). Parallel execution requires ODBC |
list of network
objects materializing specified clusters (communities) represented by
communities
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | if(interactive()) {
# undirected graph
policeGraphUn = toaGraph("dallaspolice_officer_vertices", "dallaspolice_officer_edges_un",
directed = FALSE, key = "officer", source = "officer1", target = "officer2",
vertexAttrnames = c("offense_count"), edgeAttrnames = c("weight"))
communities = computeGraphClusters(conn, policeGraphUn, type="connected",
createMembership = TRUE, includeMembership = TRUE,
distanceTableName = "public.shortestpathdistances",
membershipTableName = "public.clustermembership")
# get first 5 largest connected components as graphs
cluster_graphs = computeGraphClustersAsGraphs(conn, communities = communities, ids = 1:5)
# visualize component 2
library(GGally)
ggnet2(cluster_graphs[[2]], node.label="vertex.names", node.size="offense_count",
node.color="color", legend.position="none")
}
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