Finding Communities in Social Networks Using Graph Embeddings (Lecture Notes in Social Networks)

booksz

U P L O A D E R
ff58d982520a4b386cfeb89c5d0720ff.webp

Free Download Finding Communities in Social Networks Using Graph Embeddings (Lecture Notes in Social Networks) by Mosab Alfaqeeh, David B. Skillicorn
English | June 30, 2024 | ISBN: 3031609158 | 186 pages | PDF | 8.44 Mb
Community detection in social networks is an important but challenging problem. This book develops a new technique for finding communities that uses both structural similarity and attribute similarity simultaneously, weighting them in a principled way. The results outperform existing techniques across a wide range of measures, and so advance the state of the art in community detection. Many existing community detection techniques base similarity on either the structural connections among social-network users, or on the overlap among the attributes of each user. Either way loses useful information. There have been some attempts to use both structure and attribute similarity but success has been limited. We first build a large real-world dataset by crawling Instagram, producing a large set of user profiles. We then compute the similarity between pairs of users based on four qualitatively different profile properties: similarity of language used in posts, similarity of hashtags used (which requires extraction of content from them), similarity of images displayed (which requires extraction of what each image is 'about'), and the explicit connections when one user follows another. These single modality similarities are converted into graphs. These graphs have a common node set (the users) but different sets a weighted edges. These graphs are then connected into a single larger graph by connecting the multiple nodes representing the same user by a clique, with edge weights derived from a lazy random walk view of the single graphs. This larger graph can then be embedded in a geometry using spectral techniques. In the embedding, distance corresponds to dissimilarity so geometric clustering techniques can be used to find communities. The resulting communities are evaluated using the entire range of current techniques, outperforming all of them. Topic modelling is also applied to clusters to show that they genuinely represent users with similar interests. This can form the basis for applications such as online marketing, or key influence selection.



Code:
Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!
Links are Interchangeable - Single Extraction
 
Kommentar

In der Börse ist nur das Erstellen von Download-Angeboten erlaubt! Ignorierst du das, wird dein Beitrag ohne Vorwarnung gelöscht. Ein Eintrag ist offline? Dann nutze bitte den Link  Offline melden . Möchtest du stattdessen etwas zu einem Download schreiben, dann nutze den Link  Kommentieren . Beide Links findest du immer unter jedem Eintrag/Download.

Data-Load.me | Data-Load.ing | Data-Load.to | Data-Load.in

Auf Data-Load.me findest du Links zu kostenlosen Downloads für Filme, Serien, Dokumentationen, Anime, Animation & Zeichentrick, Audio / Musik, Software und Dokumente / Ebooks / Zeitschriften. Wir sind deine Boerse für kostenlose Downloads!

Ist Data-Load legal?

Data-Load ist nicht illegal. Es werden keine zum Download angebotene Inhalte auf den Servern von Data-Load gespeichert.
Oben Unten