The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 9,032
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
On power-law relationships of the Internet topology
Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/316188.316229 · Cited by 4,279
Self-citationBroder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., & Wiener, J. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00083-9 · Cited by 2,810
On power-law relationships of the Internet topology
Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999) · ACM SIGCOMM Computer Communication Review · doi:10.1145/316194.316229 · Cited by 2,643
Self-citationOutlier detection for high dimensional data
Aggarwal, C. C., & Yu, P. S. (2001) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/375663.375668 · Cited by 1,066
The average distances in random graphs with given expected degrees
Chung, F., & Lü, L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252631999 · Cited by 891
Cited by
Sorted by citations
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Rayana, S., & Akoglu, L. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783370 · Cited by 604
Fraud detection: A systematic literature review of graph-based anomaly detection approaches
Pourhabibi, T., Ong, K., Kam, B., & Boo, Y. L. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113303 · Cited by 518
Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., & Subrahmanian, V. S. (2018) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3159652.3159729 · Cited by 381
Self-citationFRAUDAR: Bounding Graph Fraud in the Face of Camouflage
Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939747 · Cited by 305
Self-citationVosoughi, S., Mohsenvand, M. ‘., & Roy, D. K. (2017) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3070644 · Cited by 202
Metadata
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
