|
| Titre : |
Heterogeneous Social Networks Alignment |
| Type de document : |
texte imprimé |
| Auteurs : |
Moussaoui,Salah, Auteur ; Toumi,Lyazid, Directeur de thèse |
| Editeur : |
Setif:UFA |
| Année de publication : |
2021 |
| Importance : |
1 vol (49 f .) |
| Format : |
29 cm |
| Langues : |
Français (fre) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Heterogeneous Social Networks
Network Alignment |
| Index. décimale : |
004 - Informatique |
| Résumé : |
In the last decade, the number of Social Networks has grown considerably.
Many social networks share standard known features, but each social network
platform is specialized in specific areas, leading most users to participate in
multiple social networks simultaneously. Therefore, the same communities
have been founded on different social network platforms, creating a homogeneity
of information across various social networks. Aligning users from
diverse networks helps gather information like the behavioral and interactive
differences and similarities on multiple social networks in conjunction
with Link Prediction (i.e., Friend Recommendation) and Community Detection.
The process of linking identical users across different social networks is
called Social Network Alignment, and the quality of the alignment is subject
to the amount of information acquired on the nodes (users, location, post,
etc.). Thus, we utilize more details on users to improve the alignment results.
Many social network alignment models have been suggested, the majority of
these models are based on the supervised learning approach with the help of
a labeled database of anchor links. This method could be used in a minimal
practice due to the manual extraction of the features of the anchor links,
which will evolve into a highly challenging process on extensive databases as
those present in the real world [11]. |
| Côte titre : |
MAI/0469 |
| En ligne : |
https://drive.google.com/file/d/1cCAearwTdkzCU_9ndFQqnRSh5z-I8KOk/view?usp=shari [...] |
| Format de la ressource électronique : |
pdf |
Heterogeneous Social Networks Alignment [texte imprimé] / Moussaoui,Salah, Auteur ; Toumi,Lyazid, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (49 f .) ; 29 cm. Langues : Français ( fre)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Heterogeneous Social Networks
Network Alignment |
| Index. décimale : |
004 - Informatique |
| Résumé : |
In the last decade, the number of Social Networks has grown considerably.
Many social networks share standard known features, but each social network
platform is specialized in specific areas, leading most users to participate in
multiple social networks simultaneously. Therefore, the same communities
have been founded on different social network platforms, creating a homogeneity
of information across various social networks. Aligning users from
diverse networks helps gather information like the behavioral and interactive
differences and similarities on multiple social networks in conjunction
with Link Prediction (i.e., Friend Recommendation) and Community Detection.
The process of linking identical users across different social networks is
called Social Network Alignment, and the quality of the alignment is subject
to the amount of information acquired on the nodes (users, location, post,
etc.). Thus, we utilize more details on users to improve the alignment results.
Many social network alignment models have been suggested, the majority of
these models are based on the supervised learning approach with the help of
a labeled database of anchor links. This method could be used in a minimal
practice due to the manual extraction of the features of the anchor links,
which will evolve into a highly challenging process on extensive databases as
those present in the real world [11]. |
| Côte titre : |
MAI/0469 |
| En ligne : |
https://drive.google.com/file/d/1cCAearwTdkzCU_9ndFQqnRSh5z-I8KOk/view?usp=shari [...] |
| Format de la ressource électronique : |
pdf |
|
Heterogeneous Social Networks Alignment
