Modeling Relational Data with Graph Convolutional Networks
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Detecting edge anomalies requires sensitivity to both relational semantics and temporal evolution; HGNN-based models address this by learning edge-aware representations, capturing interaction dynamics across time, and incorporating context through attention-based aggregation mechanisms.
“Detecting these anomalies requires sensitivity to both relational semantics and temporal evolution. HGNN-based models address this by learning edge-aware representations, capturing interaction dynamics across time, and incorporating context through attention-based aggregation mechanisms. [18], [33]”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [33]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe R-GCN encoder can fully encode the structural information of entities by considering all types of relations among entity connections, including in and out relations.
“In the encoder, there is an R-GCN using the idea of a graph convolutional network, which can fully encode the structural information of the entities by considering all types of relations among the entity connections, including the in and out relations.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [158]
Section 2.1 (p. 2) says the R-GCN encoder uses relation-specific transformations based on edge type and direction; its footnote says the relation set includes canonical and inverse directions. But the paper describes encoding information from local neighborhoods, with stacked layers reaching several relational steps. It does not claim the encoder fully captures all structural information about an entity. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFor link-prediction tasks, R-GCN can be regarded as an autoencoder consisting of an encoder and a decoder.
“For link-prediction tasks, a relational graph convolutional network (R-GCN) can be regarded as an autoencoder consisting of an encoder and a decoder.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [158]
Viewing SimplE as a single-vector-per-entity model makes it easily integrable (or compatible) with other embedding models in knowledge graph completion, computer vision and natural language processing.
“Viewing SimplE as a single-vector-per-entity model makes it easily integrable (or compatible) with other embedding models (in knowledge graph completion, computer vision and natural language processing) such as [35, 47, 36].”
From SimplE Embedding for Link Prediction in Knowledge Graphs (2018) · cites this paper as [36]
Many publicly available datasets are synthetic, outdated, or lack the structural and temporal complexity observed in operational systems.
“Many publicly available datasets are synthetic, outdated, or lack the structural and temporal complexity observed in operational systems [33].”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [33]
Section 5.2 (p. 5) identifies inverse training–test triples as a flaw in two benchmark datasets, but does not say that publicly available datasets are synthetic, outdated, or lack structural or temporal complexity relative to operational systems. I searched our copy for those dataset characteristics and comparisons; no passage addresses them. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Each atom and bond in a molecular graph may include features such as atomic mass, atomic number, and bond type.
“Each atom and bond may include features, such as atomic mass, atomic number, and bond type, to be included in the model [83, 166].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [83]
Small compounds are modeled as 2D and 3D molecular graphs with atoms as nodes and bonds as edges.
“Similar to proteins, small compounds are modeled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [83]
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“The architecture uses relational graph convolution (RGCNConv; Schlichtkrull et al., 2018) to learn separate weight matrices for similarity and citation edge types.”
Influential
- Authors
- Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M.
- Affiliations
- University of Amsterdam (NL)
- Vrije Universiteit Amsterdam (NL)
- Canadian Institute for Advanced Research (CA)
- Corresponding author
- Kipf, Thomas N.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 593-607
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2604314403
- Cited by
- 5,570
- References
- 39
- Citations by year
- 2026: 537 · 2025: 857 · 2024: 991 · 2023: 972 · 2022: 735 · 2021: 733 · 2020: 467 · 2019: 231 · 2018: 45 · 2017: 2
- FWCI
- 150.63
- Type
- conference paper
- Language
- en
- Open access
- green
- research.vu.nl
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Science Foundation
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (639.022.518)
- H2020 European Research Council (678254)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- relational graph convolutional network, knowledge base completion, link prediction, entity classification, knowledge graphs, multi-relational data, DistMult, factorization model, graph neural networks
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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