Leveraging social media networks for classification
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
DeepWalk outperforms other latent representation methods for creating social dimensions, especially when labeled nodes are scarce.
“DeepWalk outperforms other latent representation methods for creating social dimensions [39, 41], especially when labeled nodes are scarce.”
From DeepWalk: online learning of social representations (2014) · cites this paper as [41]
SpectralClustering generates a representation in R^d from the d-smallest eigenvectors of the normalized graph Laplacian of G.
“SpectralClustering [41]: This method generates a representation in R d from the d -smallest eigenvectors of ˜ L , the normalized graph Laplacian of G .”
From DeepWalk: online learning of social representations (2014) · cites this paper as [41]
DeepWalk learns latent social representations instead of computing statistics related to centrality or partitioning.
“We learn our latent social representations, instead of computing statistics related to centrality [12] or partitioning [41].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [41]
Most methods require global information and are offline.
“Most methods require global information and are offline [16,39-41].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [41]
The citation lists haven’t been fetched yet
References 66 · Cited by 360
- Authors
- Tang, L., & Liu, H.
- Affiliations
- Yahoo (United States) (US)
- Arizona State University (US)
- Corresponding author
- Tang, Lei
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2011-01-14
- Volume
- 23
- Issue
- 3
- Pages
- 447-478
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2168627253
- Cited by
- 360
- References
- 66
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 12 · 2023: 29 · 2022: 27 · 2021: 51 · 2020: 45 · 2019: 54 · 2018: 45 · 2017: 29 · 2016: 19 · 2015: 6 · 2014: 8 · 2013: 3 · 2012: 7
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- social media classification, network heterogeneity, social dimension, user interaction patterns, attribute-based classification, network structure learning
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