Type: Package
Title: Exploring Social Network Structures Through Friendship-Driven Community Detection with Association Rules Mining
Version: 1.0.5
Maintainer: Mohamed El-Moussaoui <med.elmoussaoui.ced@gmail.com>
Description: Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.
License: GPL-3
URL: https://github.com/assuom44/arlclustering
BugReports: https://github.com/assuom44/arlclustering/issues
LazyLoad: false
Encoding: UTF-8
RoxygenNote: 7.2.3
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, tidyr
Imports: methods, igraph, arules, grDevices, graphics, stats
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-09-08 23:12:38 UTC; wadii
Author: Mohamed El-Moussaoui [aut, cre], Mohamed Hanine [aut], Ali Kartit [ths], Tarik Agouti [rev]
Repository: CRAN
Date/Publication: 2024-09-11 15:00:02 UTC

Calculate the Mode of a Vector

Description

This function calculates the mode of a vector.

Usage

arlc_calculate_mode(x)

Arguments

x

A vector.

Value

The mode of the vector.

Examples

arlc_calculate_mode(c(1, 2, 2, 3, 4))

Clean Final Rules

Description

This function cleans the final set of association rules.

Usage

arlc_clean_final_rules(final_rules)

Arguments

final_rules

A set of final rules to be cleaned.

Value

A cleaned set of rules.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)
cleaned_rules <- arlc_clean_final_rules(NonRRSig_rules$FiltredRules)
message(cleaned_rules)

Plot Graph with Custom Layout and Communities

Description

This function plots a graph with specified aesthetics and highlights communities.

Usage

arlc_clusters_plot(g, graphLabel, clusters)

Arguments

g

An igraph object representing the graph.

graphLabel

A character string for the graph label to be displayed in the title.

clusters

A list of clusters to highlight in the plot.

Value

The function produces a plot as a side effect.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)
cleaned_rules <- arlc_clean_final_rules(NonRRSig_rules$FiltredRules)
clusters <- arlc_generate_clusters(cleaned_rules)
arlc_clusters_plot(g$graph, "Karate Club", clusters$Clusters)

Convert a Date to a Different Format

Description

This function converts a date from one format to another.

Usage

arlc_convert_date_format(date_str, from_format, to_format)

Arguments

date_str

A date string.

from_format

The current format of the date string.

to_format

The desired format of the date string.

Value

The date string in the new format.

Examples

arlc_convert_date_format("2023-01-01", "%Y-%m-%d", "%d-%m-%Y")

Count NA Values in a Data Frame

Description

This function counts the number of NA values in each column of a data frame.

Usage

arlc_count_na(df)

Arguments

df

A data frame.

Value

A named vector with the count of NA values in each column.

Examples

arlc_count_na(data.frame(a = c(1, NA, 3), b = c(NA, NA, 3)))

Create a Summary of a Data Frame

Description

This function creates a summary of a data frame including count, mean, median, and standard deviation for numeric columns.

Usage

arlc_df_summary(df)

Arguments

df

A data frame.

Value

A data frame summarizing the statistics of the input data frame.

Examples

arlc_df_summary(data.frame(a = c(1, 2, 3, 4, 5), b = c(5, 4, 3, 2, 1)))

Clean Transactions by Removing Overlapping Sets

Description

This function processes a list of sets and removes those that are fully overlapped by other sets.

Usage

arlc_fct_clean_transactions(all_sets)

Arguments

all_sets

A list of sets where each set is a vector of elements.

Details

The function iterates through each set and checks if it is fully overlapped by any other set. If a set is fully overlapped, it is excluded from the final list of sets. The result is a list of sets with no fully overlapped sets.

Value

A list of sets with fully overlapped sets removed.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)
cleaned_rules <- arlc_clean_final_rules(NonRRSig_rules$FiltredRules)

vec <- lapply(cleaned_rules, function(v) unique(unlist(v)))
vec2 <- split(vec, sapply(vec, `[`, 1))
sorted_result <- lapply(vec2, function(v) sort(unique(unlist(v))))

clusters <- arlc_fct_clean_transactions(sorted_result)

message (clusters)

Get Best Apriori Thresholds

Description

This function finds the best support and confidence thresholds for the Apriori algorithm to maximize the lift of the generated association rules.

Usage

arlc_fct_get_best_apriori_thresholds(transactions, support_range, conf)

Arguments

transactions

A transaction dataset of class transactions from the arules package.

support_range

A numeric vector specifying the range of support values to be tested.

conf

A numeric value (0.5 or 1.0) specifying the confidence value.

Details

This function iterates through the given ranges of support and confidence values, applies the Apriori algorithm to find association rules for each pair of values, and selects the pair that produces rules with the highest lift.

Value

A numeric vector containing the best support, best confidence, highest lift, and the number of rules found. The return value is a named vector with elements best_support, best_confidence, best_lift, and len_rules.

Examples


library(arlclustering)
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
best_thresholds <- arlc_fct_get_best_apriori_thresholds(trans, supportRange, Conf)

Check if a File Exists and is Readable

Description

This function checks if a file exists and is readable.

Usage

arlc_file_exists_readable(filepath)

Arguments

filepath

The path to the file.

Value

TRUE if the file exists and is readable, FALSE otherwise.

Examples

arlc_file_exists_readable("example.txt")

Get Gross Rules

Description

This function generates gross association rules from transactions.

Usage

arlc_gen_gross_rules(transactions, minSupp, minConf, minLenRules, maxLenRules)

Arguments

transactions

A transactions object.

minSupp

Minimum support threshold.

minConf

Minimum confidence threshold.

minLenRules

Minimum length of rules.

maxLenRules

Maximum length of rules.

Value

A set of gross association rules.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)

Get Transactional Dataset

Description

This function generates a transactional dataset from a graph.

Usage

arlc_gen_transactions(graph)

Arguments

graph

A graph object.

Value

A transactional dataset.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)

Generate Clusters

Description

This function takes a vector of preprocessed rules, combines elements starting with the same value, groups segments by the starting value, sorts elements within each segment, and returns potential clusters.

Usage

arlc_generate_clusters(vec)

Arguments

vec

A vector of preprocessed rules.

Details

This function generates potential clusters based on preprocessed rules.

Value

A list of unique and potential clusters.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)
cleaned_rules <- arlc_clean_final_rules(NonRRSig_rules$FiltredRules)
clusters <- arlc_generate_clusters(cleaned_rules)

Generate a Sequence of Dates

Description

This function generates a sequence of dates between two given dates.

Usage

arlc_generate_date_sequence(start_date, end_date, by = "day")

Arguments

start_date

The start date.

end_date

The end date.

by

The step size for the sequence (e.g., "day", "week", "month").

Value

A vector of dates.

Examples

arlc_generate_date_sequence("2023-01-01", "2023-01-10", "day")

Generate a Unique Identifier

Description

This function generates a unique identifier string.

Usage

arlc_generate_uid(length = 10)

Arguments

length

The length of the unique identifier. Default is 10.

Value

A unique identifier string.

Examples

arlc_generate_uid()
arlc_generate_uid(15)

Get Non-Redundant Rules

Description

This function takes a set of gross rules, removes redundant rules, and returns the total number of non-redundant rules along with the non-redundant rules.

Usage

arlc_get_NonR_rules(gross_rules)

Arguments

gross_rules

A vector or dataframe of gross rules.

Details

This function cleans the gross rules and provides non-redundant rules.

Value

A list containing the total number of non-redundant rules and the non-redundant rules.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)

Get Apriori Thresholds

Description

This function takes a transaction dataset and ranges for support and confidence, computes the best thresholds, and returns the best minimum support, minimum confidence, best lift, total number of gross rules, and ratio of generated rules to total number of transactions.

Usage

arlc_get_apriori_thresholds(trx, supportRange, Conf)

Arguments

trx

A transaction dataset of class transactions from the arules package.

supportRange

A sequence of values representing the range for minimum support.

Conf

A sequence of values representing the range for minimum confidence.

Details

This function generates gross rules based on the best obtained thresholds.

This function iterates through the given ranges of support and confidence values, applies the Apriori algorithm to find association rules for each pair of values, and selects the pair that produces rules with the highest lift. The function then returns the best thresholds along with the lift, number of rules, and their ratio to the total transactions.

Value

A list containing:

minSupp

The best minimum support value.

minConf

The best minimum confidence value.

bestLift

The highest lift value obtained.

lenRules

The total number of gross rules generated.

ratio

The ratio of generated rules to the total number of transactions.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
message(params$minSupp)
message(params$minConf)
message(params$bestLift)
message(params$lenRules)
message(params$ratio)

Get Network Dataset

Description

This function reads a network dataset from a GML file, assigns node names, and calculates various properties of the graph such as total edges, total nodes, and average degree.

Usage

arlc_get_network_dataset(file_path, label)

Arguments

file_path

The file path to the GML file to be loaded.

label

A label for the graph.

Details

This function loads a network dataset from a specified GML file and computes basic graph properties.

Value

A list containing the graph object and its properties: total edges, total nodes, and average degree.

Examples


# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
loaded_karate <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
message(loaded_karate$graph)
message(loaded_karate$graphLabel)
message(loaded_karate$totalEdges)
message(loaded_karate$graphEdges)
message(loaded_karate$totalNodes)
message(loaded_karate$graphNodes)
message(loaded_karate$averageDegree)

Get Significant Rules

Description

This function takes all transactions and a set of non-redundant rules as input, and returns significant rules based on a specified method and adjustment.

Usage

arlc_get_significant_rules(all_trans, nonRR_rules)

Arguments

all_trans

A dataframe containing all transactions.

nonRR_rules

A list of non-redundant rules.

Details

This function filters significant rules from a set of non-redundant rules.

Value

A list containing the total number of significant non-redundant rules and the significant rules themselves.

Examples


library(arlclustering)
# Create a sample transactions dataset
sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
trans <- arlc_gen_transactions(g$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)

Check if a Vector is Numeric

Description

This function checks if all elements of a vector are numeric.

Usage

arlc_is_numeric_vector(x)

Arguments

x

A vector.

Value

TRUE if all elements are numeric, FALSE otherwise.

Examples

arlc_is_numeric_vector(c(1, 2, 3))
arlc_is_numeric_vector(c(1, "a", 3))

Convert List of Vectors to Data Frame

Description

This function converts a list of named vectors to a data frame.

Usage

arlc_list_to_df(lst)

Arguments

lst

A list of named vectors.

Value

A data frame with each element of the list as a row.

Examples

lst <- list(a = c(x = 1, y = 2), b = c(x = 3, y = 4))
arlc_list_to_df(lst)

Measure Execution Time of a Function

Description

This function measures the execution time of a given function.

Usage

arlc_measure_time(func, ...)

Arguments

func

The function to measure.

...

Additional arguments to pass to the function.

Value

The result of the function execution.

Examples

arlc_measure_time(Sys.sleep, 1)

Normalize a Numeric Vector

Description

This function normalizes a numeric vector to have values between 0 and 1.

Usage

arlc_normalize_vector(x)

Arguments

x

A numeric vector.

Value

A normalized numeric vector.

Examples

arlc_normalize_vector(c(1, 2, 3, 4, 5))

Replace NA with a Specified Value

Description

This function replaces NA values in a vector or data frame with a specified value.

Usage

arlc_replace_na(x, value)

Arguments

x

A vector or data frame.

value

The value to replace NA with.

Value

The vector or data frame with NA values replaced.

Examples

arlc_replace_na(c(1, NA, 3), 0)
arlc_replace_na(data.frame(a = c(1, NA, 3), b = c(NA, NA, 3)), 0)