CatchSync: catching synchronized behavior in large directed graphs
Abstract
Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns show up in many applications, from Twitter users who buy fake followers, manipulating the social network, to botnet members performing distributed denial of service attacks, disturbing the network traffic graph. We propose a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters: (a) synchronized behavior: suspicious nodes have extremely similar behavior pattern, because they are often required to perform some task together (such as follow the same user); and (b) rare behavior: their connectivity patterns are very different from the majority. We introduce novel measures to quantify both concepts ("synchronicity" and "normality") and we propose a parameter-free algorithm that works on the resulting synchronicity-normality plots. Thanks to careful design, CatchSync has the following desirable properties: (a) it is scalable to large datasets, being linear on the graph size; (b) it is parameter free; and (c) it is side-information-oblivious: it can operate using only the topology, without needing labeled data, nor timing information, etc., while still capable of using side information, if available. We applied CatchSync on two large, real datasets 1-billion-edge Twitter social graph and 3-billion-edge Tencent Weibo social graph, and several synthetic ones; CatchSync consistently outperforms existing competitors, both in detection accuracy by 36% on Twitter and 20% on Tencent Weibo, as well as in speed.
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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 [23]
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- Authors
- Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S.
- Affiliations
- Tsinghua University (CN)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 941-950
- OpenAlex
- W2101447063
- Cited by
- 151
- References
- 41
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 5 · 2022: 6 · 2021: 15 · 2020: 20 · 2019: 12 · 2018: 19 · 2017: 32 · 2016: 16 · 2015: 14
- FWCI
- 19.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- Division of Computer and Network Systems (CNS-1314632)
- Army Research Office (W911NF-11-C-0088)
- U.S. Army Research Laboratory (W911NF-09-2-0053)
- Media Development Authority - Singapore (WBS:R-252-300-001-490)
- National Natural Science Foundation of China (61210008, 61303075, 61370022)
- Defense Advanced Research Projects Agency (W911NF-11-C-0088)
- Ministry of Science and Technology of the People's Republic of China (2013DFG12870, 2011CB302206)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- directed graphs, synchronicity, normality, connectivity patterns, bot detection, fake followers, parameter-free algorithms, Weibo, large-scale graphs
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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