Inferring Strange Behavior from Connectivity Pattern in Social Networks
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The paper's work can complement other methods used in prior work to detect fake and compromised accounts, such as unsupervised anomaly detection, temporal features, IP addresses, and generic supervised learning.
“Finally, we highlight that our work can complement other methods used in prior work to detect fake and compromised accounts, such as using unsupervised anomaly detection techniques [50], temporal features [23, 24], IP addresses [45], as well as generic supervised learning [3].”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [24]
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References 20 · Cited by 82
- Authors
- Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S.
- Affiliations
- Tsinghua University (CN)
- Carnegie Mellon University (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 126-138
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W81017276
- Cited by
- 82
- References
- 20
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 7 · 2021: 7 · 2020: 8 · 2019: 7 · 2018: 13 · 2017: 12 · 2016: 9 · 2015: 10 · 2014: 3
- FWCI
- 10.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Keywords
- social networks, adjacency matrix, spectral subspaces, anomaly detection, user behavior detection, block structure, staircase structure, large-scale graphs
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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