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ARLClustering - Testing Facebook L1 Organization dataset

library(arlclustering)
#library(igraph)

Dataset description

The Facebook L1 organization social network dataset is a gml file, containing 5793 nodes and 45266 edges.

Loading network dataset

# Start the timer
t1 <- system.time({
  dataset_path <- system.file("extdata", "Facebook_Org_L1.gml", package = "arlclustering")
  if (dataset_path == "") {
    stop("Facebook_Org_L1.gml file not found")
  }
  
  g <- arlc_get_network_dataset(dataset_path, "Facebook L1 Organization")
  g$graphLabel
  g$totalNodes
  g$totalEdges
  g$averageDegree
})

# Display the total processing time
message("Graph loading Processing Time: ", t1["elapsed"], " seconds\n")
#> Graph loading Processing Time: 0.360999999999999 seconds

Generate Transactions

Next, we generate transactions from the graph, with a total rows of 3541.

# Start the timer
t2 <- system.time({
  transactions <- arlc_gen_transactions(g$graph)
  transactions
})

# Display the total processing time
message("Transaction dataset Processing Time: ", t2["elapsed"], " seconds\n")
#> Transaction dataset Processing Time: 2.795 seconds

Get Apriori Thresholds

We obtain the apriori thresholds for the generated transactions. The following are the thresholds for the apriori execution: - The Minimum Support : 0.002 - The Minimum Confidence : 0.5 - The Lift : 442.625 - The Gross Rules length : 97825 - The selection Ratio : 28

# Start the timer
t3 <- system.time({
  params <- arlc_get_apriori_thresholds(transactions,
                                        supportRange = seq(0.002, 0.003, by = 0.001),
                                        Conf = 0.5)
  params$minSupp
  params$minConf
  params$bestLift
  params$lenRules
  params$ratio
})

# Display the total processing time
message("Graph loading Processing Time: ", t3["elapsed"], " seconds\n")
#> Graph loading Processing Time: 0.189 seconds

Generate Gross Rules

We use the obtained parameters to generate gross rules, where we obtain 97858 rules.

# Start the timer
t4 <- system.time({
  minLenRules <- 1
  maxLenRules <- params$lenRules
  if (!is.finite(maxLenRules) || maxLenRules > 5*length(transactions)) {
    maxLenRules <- 5*length(transactions)
  }
  
  grossRules <- arlc_gen_gross_rules(transactions,
                                     minSupp = params$minSupp,
                                     minConf = params$minConf,
                                     minLenRules = minLenRules+1,
                                     maxLenRules = maxLenRules)
  grossRules$TotalRulesWithLengthFilter
})
#> Apriori
#> 
#> Parameter specification:
#>  confidence minval smax arem  aval originalSupport maxtime support minlen
#>         0.5    0.1    1 none FALSE            TRUE       5   0.002      2
#>  maxlen target  ext
#>   17705  rules TRUE
#> 
#> Algorithmic control:
#>  filter tree heap memopt load sort verbose
#>     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
#> 
#> Absolute minimum support count: 7 
#> 
#> set item appearances ...[0 item(s)] done [0.00s].
#> set transactions ...[5713 item(s), 3541 transaction(s)] done [0.01s].
#> sorting and recoding items ... [952 item(s)] done [0.00s].
#> creating transaction tree ... done [0.00s].
#> checking subsets of size 1 2 3 4 5 6 7 8 9 10 11 done [0.05s].
#> writing ... [97858 rule(s)] done [0.03s].
#> creating S4 object  ... done [0.03s].
# Display the total number of clusters and the total processing time
message("Gross rules generation Time: ", t4["elapsed"], " seconds\n")
#> Gross rules generation Time: 0.166 seconds

Filter Significant and Non-Redundant Rules

We filter out redundant rules from the generated gross rules. Next, we filter out non-significant rules from the non-redundant rules, and we obtain the 18907 rule items.

t5 <- system.time({
  NonRedRules <- arlc_get_NonR_rules(grossRules$GrossRules)
  NonRSigRules <- arlc_get_significant_rules(transactions,
                                             NonRedRules$FiltredRules)
  NonRSigRules$TotFiltredRules
})
# Display the total number of clusters and the total processing time
message("\nClearing rules Processing Time: ", t5["elapsed"], " seconds\n")
#> 
#> Clearing rules Processing Time: 4.496 seconds

Clean and genarate final Rules

We clean the final set of rules to prepare for clustering. Then, we generate clusters based on the cleaned rules. The total identified clusters is 190 clusters.

t6 <- system.time({
  cleanedRules <- arlc_clean_final_rules(NonRSigRules$FiltredRules)
  clusters <- arlc_generate_clusters(cleanedRules)
  clusters$TotClusters
})
# Display the total number of clusters and the total processing time
message("Cleaning final rules Processing Time: ", t6["elapsed"], " seconds\n")
#> Cleaning final rules Processing Time: 1.624 seconds

message("The total comsumed time is:",t1["elapsed"]+ t2["elapsed"]+t3["elapsed"]+t4["elapsed"]+t5["elapsed"]+t6["elapsed"], "seconds\n")
#> The total comsumed time is:9.631seconds

Plot Clusters

Finally, we visualize the identified clusters.

arlc_clusters_plot(g$graph,
                   g$graphLabel,
                   clusters$Clusters)
#> 
#> Total Identified Clusters: 190
#>  =========================
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#>  =========================

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