On the public citation graph
Degan - Decomposition-Based Unified Anomaly Detection in Static Networks
0 signed · 27 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-extracted
Generative Adversarial Nets
inferredcites →https://doi.org/10.1016/0378-8733(83)90028-xopenalex-extracted
Network structure and minimum degree
inferredcites →https://doi.org/10.1109/icdm.2013.88openalex-extracted
Statistical Selection of Congruent Subspaces for Mining Attributed Graphs
inferredcites →https://doi.org/10.1109/icdm.2018.00072openalex-extracted
Deep Structure Learning for Fraud Detection
inferredcites →https://doi.org/10.1109/icdm50108.2020.00116openalex-extracted
AANE: Anomaly Aware Network Embedding for Anomalous Link Detection
inferredcites →https://doi.org/10.1109/ijcnn.2005.1555942openalex-extracted
A new model for learning in graph domains
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9206720openalex-extracted
Unified Graph Embedding-Based Anomalous Edge Detection
inferredcites →https://doi.org/10.1109/tkde.2020.3015098openalex-extracted
A Deep Multi-View Framework for Anomaly Detection on Attributed Networks
inferredcites →https://doi.org/10.1109/tkde.2021.3118815openalex-extracted
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-extracted
The Graph Neural Network Model
inferredcites →https://doi.org/10.1137/1.9781611974348.24openalex-extracted
Scalable Anomaly Ranking of Attributed Neighborhoods
inferredcites →https://doi.org/10.1137/1.9781611975673.67openalex-extracted
Deep Anomaly Detection on Attributed Networks
inferredcites →https://doi.org/10.1145/1232722.1232727openalex-extracted
The dynamics of viral marketing
inferredcites →https://doi.org/10.1145/1557019.1557109openalex-extracted
Relational learning via latent social dimensions
inferredcites →https://doi.org/10.1145/3336191.3371788openalex-extracted
Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding
inferredcites →https://doi.org/10.1145/3340531.3412070openalex-extracted
Generative Adversarial Attributed Network Anomaly Detection
inferredcites →https://doi.org/10.1145/3474379openalex-extracted
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
inferredcites →https://doi.org/10.1145/3633518openalex-extracted
A Survey on Graph Representation Learning Methods
inferredcites →https://doi.org/10.24963/ijcai.2017/299openalex-extracted
Radar: Residual Analysis for Anomaly Detection in Attributed Networks
inferredcites →https://doi.org/10.24963/ijcai.2018/488openalex-extracted
ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks
inferredcites →https://doi.org/10.24963/ijcai.2020/179openalex-extracted
Inductive Anomaly Detection on Attributed Networks
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-extracted
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-extracted
Semi-Supervised Classification with Graph Convolutional Networks
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.