Finding Communities in Social Networks Using Graph Embeddings
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 acro...
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| Awdur Corfforaethol: | |
| Fformat: | Electronig eLyfr |
| Iaith: | Saesneg |
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Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Rhifyn: | 1st ed. 2024. |
| Cyfres: | Lecture Notes in Social Networks,
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| Pynciau: | |
| Mynediad Ar-lein: | https://doi.org/10.1007/978-3-031-60916-9 |
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| 100 | 1 | |a Alfaqeeh, Mosab. |e author. |0 (orcid)0009-0009-0890-8332 |1 https://orcid.org/0009-0009-0890-8332 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Finding Communities in Social Networks Using Graph Embeddings |h [electronic resource] / |c by Mosab Alfaqeeh, David B. Skillicorn. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (IX, 177 p. 90 illus.). |b 34 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
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| 490 | 1 | |a Lecture Notes in Social Networks, |x 2190-5436 | |
| 505 | 0 | |a Chapter 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 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Social sciences |x Data processing. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application software. | |
| 650 | 1 | 4 | |a Computer Application in Social and Behavioral Sciences. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 700 | 1 | |a Skillicorn, David B. |e author. |0 (orcid)0000-0003-0605-4029 |1 https://orcid.org/0000-0003-0605-4029 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031609152 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031609176 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031609183 |
| 830 | 0 | |a Lecture Notes in Social Networks, |x 2190-5436 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-60916-9 |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||