04058nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001000153072001500163072002300178072001500201100007800216245013200294250001800426264006900444300006400513336002600577337002600603338003600629347002400665490004900689505020700738520207900945650003803024650002203062650002603084650006003110650002203170650005103192700008203243710003403325773002603359776003603385776003603421776003603457830004903493856004603542978-3-031-60916-9DE-He21320251030112341.0cr nn 008mamaa240629s2024    sz |    s    |||| 0|eng d  a97830316091697 a10.1007/978-3-031-60916-92doi 4aH61.3 7aUF2bicssc 7aCOM0050002bisacsh 7aUXJ2thema1 aAlfaqeeh, Mosab.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aFinding Communities in Social Networks Using Graph Embeddingsh[electronic resource] /cby Mosab Alfaqeeh, David B. Skillicorn.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aIX, 177 p. 90 illus., 34 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Social Networks,x2190-54360 aChapter 1: Introduction -- Chapter 2: Background -- Chapter 3: Building blocks -- Chapter 4: Social network data -- Chapter 5: Methodology -- Chapter 6: Results and validation -- Chapter 7: Conclusions.  aCommunity 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. 0aSocial sciencesxData processing. 0aMachine learning. 0aApplication software.14aComputer Application in Social and Behavioral Sciences.24aMachine Learning.24aComputer and Information Systems Applications.1 aSkillicorn, David B.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303160915208iPrinted edition:z978303160917608iPrinted edition:z9783031609183 0aLecture Notes in Social Networks,x2190-543640uhttps://doi.org/10.1007/978-3-031-60916-9