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ARLClustering - General Introduction

library(arlclustering)

Paper abstract

This paper presents ARLClustering, an R package that introduces a novel approach for community detection in social networks using data mining techniques, particularly the Apriori algorithm. Building on the foundation of existing research, ARLClustering leverages association rules to identify and analyze meaningful communities based on node interactions. The package provides a suite of functions tailored for social network analysis and data mining, tested on various synthetic and real-world datasets such as the Karate Club, Dolphins, LesMiserables, and facebook networks. By addressing the gap in open-source implementations of association rule-based community detection, ARLClustering serves as a crucial tool for both researchers and practitioners. It bridges theoretical insights with practical utility, promoting advancements in methodologies for community detection in social networks.

Experimentation summary

Activity preparation:

test_results <- data.frame(
Network = c("Karate Club","Dolphins","LesMiserables","Word Adjacencies","Facebook-Friends","NetScience","Facebook L1 Org."),
  Tot.Nodes   = c(   34,    62,    77,   112,    362,     1589,   5793),
  Tot.Edges   = c(   78,   159,   254,   425,   1988,     2742,  45266),
  min.Supp    = c(  0.1,  0.05,  0.04,  0.03,   0.04,    0.011,  0.002),
  min.Conf    = c(  0.5,   0.5,   0.5,   0.5,    0.5,      0.5,    0.5),
  Tot.Rules   = c(   66,   201, 51774,   649,  74748,   875908,  97858),
  Communities = c(   12,    17,     7,    20,     20,        4,    190),
  Time.s      = c(0.276, 0.455, 1.485, 0.587,  3.501,   14.646, 12.335)
)

Obtained results:

knitr::kable(test_results, caption = "ARLClustering Test Results Summary")
ARLClustering Test Results Summary
Network Tot.Nodes Tot.Edges min.Supp min.Conf Tot.Rules Communities Time.s
Karate Club 34 78 0.100 0.5 66 12 0.276
Dolphins 62 159 0.050 0.5 201 17 0.455
LesMiserables 77 254 0.040 0.5 51774 7 1.485
Word Adjacencies 112 425 0.030 0.5 649 20 0.587
Facebook-Friends 362 1988 0.040 0.5 74748 20 3.501
NetScience 1589 2742 0.011 0.5 875908 4 14.646
Facebook L1 Org. 5793 45266 0.002 0.5 97858 190 12.335

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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