TY - JOUR
T1 - Detecting implicit cross-communities to which an active user belongs
AU - Taha, Kamal
AU - Yoo, Paul
AU - Eddinari, Fatima Zohra
N1 - Publisher Copyright:
© 2022 Taha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/4
Y1 - 2022/4
N2 - Most realistic social communities are multi-profiled cross-communities constructed from users sharing commonalities that include adaptive social profile ingredients (i.e., natural adaptation to certain social traits). The most important types of such cross-communities are the densest holonic ones, because they exhibit many interesting properties. For example, such a cross-community can represent a portion of users, who share all the following traits: ethnicity, religion, neighbourhood, and age-range. The denser a multi-profiled cross-community is, the more granular and holonic it is and the greater the number of its members, whose interests are exhibited in the common interests of the entire cross-community. Moreover, the denser a cross-community is, the more specific and distinguishable its interests are (e.g., more distinguishable from other cross-communities). Unfortunately, methods that advocate the detection of granular multi-profiled cross-communities have been under-researched. Most current methods detect multi-profiled communities without consideration to their granularities. To overcome this, we introduce in this paper a novel methodology for detecting the smallest and most granular multi-profiled cross-community, to which an active user belongs. The methodology is implemented in a system called ID_CC. To improve the accuracy of detecting such cross-communities, we first uncover missing links in social networks. It is imperative for uncovering such missing links because they may contain valuable information (social characteristics commonalities, cross-memberships, etc.). We evaluated ID_CC by comparing it experimentally with eight methods. The results of the experiments revealed marked improvement.
AB - Most realistic social communities are multi-profiled cross-communities constructed from users sharing commonalities that include adaptive social profile ingredients (i.e., natural adaptation to certain social traits). The most important types of such cross-communities are the densest holonic ones, because they exhibit many interesting properties. For example, such a cross-community can represent a portion of users, who share all the following traits: ethnicity, religion, neighbourhood, and age-range. The denser a multi-profiled cross-community is, the more granular and holonic it is and the greater the number of its members, whose interests are exhibited in the common interests of the entire cross-community. Moreover, the denser a cross-community is, the more specific and distinguishable its interests are (e.g., more distinguishable from other cross-communities). Unfortunately, methods that advocate the detection of granular multi-profiled cross-communities have been under-researched. Most current methods detect multi-profiled communities without consideration to their granularities. To overcome this, we introduce in this paper a novel methodology for detecting the smallest and most granular multi-profiled cross-community, to which an active user belongs. The methodology is implemented in a system called ID_CC. To improve the accuracy of detecting such cross-communities, we first uncover missing links in social networks. It is imperative for uncovering such missing links because they may contain valuable information (social characteristics commonalities, cross-memberships, etc.). We evaluated ID_CC by comparing it experimentally with eight methods. The results of the experiments revealed marked improvement.
UR - http://www.scopus.com/inward/record.url?scp=85128608218&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0264771
DO - 10.1371/journal.pone.0264771
M3 - Article
C2 - 35439250
AN - SCOPUS:85128608218
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 4 April
M1 - e0264771
ER -