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020 _a9783031609169
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024 7 _a10.1007/978-3-031-60916-9
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072 7 _aCOM005000
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082 0 4 _a300.00285
_223
100 1 _aAlfaqeeh, Mosab.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9104620
245 1 0 _aFinding Communities in Social Networks Using Graph Embeddings
_h[electronic resource] /
_cby Mosab Alfaqeeh, David B. Skillicorn.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aIX, 177 p. 90 illus., 34 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Social Networks,
_x2190-5436
505 0 _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.
520 _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.
650 0 _aSocial sciences
_xData processing.
_983360
650 0 _aMachine learning.
_91831
650 0 _aApplication software.
_9104623
650 1 4 _aComputer Application in Social and Behavioral Sciences.
_931815
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer and Information Systems Applications.
_9104626
700 1 _aSkillicorn, David B.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9104627
710 2 _aSpringerLink (Online service)
_9104629
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031609152
776 0 8 _iPrinted edition:
_z9783031609176
776 0 8 _iPrinted edition:
_z9783031609183
830 0 _aLecture Notes in Social Networks,
_x2190-5436
_9104630
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60916-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c88429
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