Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:18:23.084662Z
Paper Citation Record · LEDGER
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
As of 16 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2509.06609.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:12:02.322390Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T19:12:02.441708Z
Reference resolution
53 of 53 outbound references displayed
- verified exact0
- verified fuzzy53
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2ac12df8-1a46-4752-8d9e-4621e0bd7796 · outbound
This paper cites How to use graph data in the wild to help graph anomaly detection? InKDD, page 61–72, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models How to use graph data in the wild to help graph anomaly detection? InKDD, page 61–72, 2025
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.516118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6f323ce5-7a3f-4c3d-ae3f-0877d98e6da1 · outbound
This paper cites Wirjanto, and Qinmu Peng.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Wirjanto, and Qinmu Peng
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.449144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2c080474-c40d-47da-ab40-8305a0188641 · outbound
This paper cites Divide and denoise: Empowering simple models for robust semi-supervised node classification against label noise.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Divide and denoise: Empowering simple models for robust semi-supervised node classification against label noise
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.357330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8b179126-af0e-4237-921f-ac17b4a30305 · outbound
This paper cites Cross- domain graph anomaly detection.TNNLS, 33(6):2406–2415, 2021.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Cross- domain graph anomaly detection.TNNLS, 33(6):2406–2415, 2021
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.312030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 265862ff-ac70-4b21-a70a-81b3654e47a5 · outbound
This paper cites Simultaneously detecting node and edge level anomalies on heterogeneous attributed graphs.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Simultaneously detecting node and edge level anomalies on heterogeneous attributed graphs
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.298823Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c5697bd4-2e4c-4336-9660-7541cbee6eba · outbound
This paper cites Alleviating structural distribution shift in graph anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Alleviating structural distribution shift in graph anomaly detection
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.283690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e6e986dc-67ec-475a-983a-7de4684280bb · outbound
This paper cites Anomaly detection in the open world: Normality shift detection, explanation, and adaptation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Anomaly detection in the open world: Normality shift detection, explanation, and adaptation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.270154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7678a219-b778-4398-b608-6a2f19c7758a · outbound
This paper cites Hybrid-order anomaly detection on attributed networks.TKDE, 35(12):12249–12263, 2021.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Hybrid-order anomaly detection on attributed networks.TKDE, 35(12):12249–12263, 2021
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.228075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2b62f005-6a34-4c51-a29c-6fff66151a67 · outbound
This paper cites Assemble your crew: Automatic multi-agent communication topology design via autoregressive graph generation.arXiv, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Assemble your crew: Automatic multi-agent communication topology design via autoregressive graph generation.arXiv, 2025
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.159414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation acea3628-52b5-4b63-82d4-12f2fe57d92e · outbound
This paper cites Graph anomaly detection with domain-agnostic pre-training and few-shot adaptation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Graph anomaly detection with domain-agnostic pre-training and few-shot adaptation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.056768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 837e3728-6f14-4757-9134-a8859a94909b · outbound
This paper cites Relevance-aware anomalous users detection in social network via graph neural network.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Relevance-aware anomalous users detection in social network via graph neural network
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.033740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 324f7073-e8da-4e97-9a13-d3e10cb00c58 · outbound
This paper cites Cross- domain graph level anomaly detection.TKDE, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Cross- domain graph level anomaly detection.TKDE, 2024
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:25.006858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3ed13f9a-ae15-4efa-ad8f-8eb8e6f4da8f · outbound
This paper cites Vicky Zhao, Yuan Yao, and Jia Li.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Vicky Zhao, Yuan Yao, and Jia Li
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.987334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4bb2a2e0-8cbc-4e44-a3ce-7ad02b49b3eb · outbound
This paper cites Bourne: Bootstrapped self-supervised learning framework for unified graph anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Bourne: Bootstrapped self-supervised learning framework for unified graph anomaly detection
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.973683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ef873f57-b55d-42a3-b9d2-3bd3782ac2b3 · outbound
This paper cites Pygod: A python library for graph outlier detection.JMLR, 25(141):1–9, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Pygod: A python library for graph outlier detection.JMLR, 25(141):1–9, 2024
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.960205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f483e409-af3e-4536-abb0-6eddbf20b81a · outbound
This paper cites Bond: Benchmarking unsupervised outlier node detection on static attributed graphs.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Bond: Benchmarking unsupervised outlier node detection on static attributed graphs
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.916360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 35a2cbba-cf41-475d-a8d0-bb2e55909a30 · outbound
This paper cites Anomalyllm: Few-shot anomaly edge detection for dynamic graphs using large language models.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Anomalyllm: Few-shot anomaly edge detection for dynamic graphs using large language models
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.854262Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d0bb1d40-b3fc-4ae8-90b9-c4a413060db6 · outbound
This paper cites Test-time adaptation on recommender system with data-centric graph transformation.IJCAI, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Test-time adaptation on recommender system with data-centric graph transformation.IJCAI, 2025
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.840804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ffd75202-09dd-48ce-8b02-e94bfea5a4dd · outbound
This paper cites Self-supervision improves diffusion models for tabular data imputation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Self-supervision improves diffusion models for tabular data imputation
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.825153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fc4bed9e-43a6-4443-b8ae-e7a4b1c46040 · outbound
This paper cites Towards self-interpretable graph-level anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Towards self-interpretable graph-level anomaly detection
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.811361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 71974e9f-b5f5-42ba-bcac-3e7ee7398e25 · outbound
This paper cites ARC: A generalist graph anomaly detector with in-context learning.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models ARC: A generalist graph anomaly detector with in-context learning
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.773588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 54695c56-dac3-4ce7-a026-f04ba83b9d19 · outbound
This paper cites Anomaly detection on attributed networks via contrastive self- supervised learning.TNNLS, 33(6):2378–2392, 2021.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Anomaly detection on attributed networks via contrastive self- supervised learning.TNNLS, 33(6):2378–2392, 2021
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.682304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8a477e86-1495-4362-bfa3-fd7a1a0cb63c · outbound
This paper cites Graph- augmented large language model agents: Current progress and future prospects.arXiv, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Graph- augmented large language model agents: Current progress and future prospects.arXiv, 2025
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.634989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d71ee8b2-f46d-469b-a0a4-492091e9f5ec · outbound
This paper cites Grace: Empowering llm-based software vulnerability detection with graph structure and in-context learning.Journal of Systems and Software, 212:112031, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Grace: Empowering llm-based software vulnerability detection with graph structure and in-context learning.Journal of Systems and Software, 212:112031, 2024
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.620748Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d150f04b-5b17-43d6-b924-0ca2de4dd38e · outbound
This paper cites A comprehensive survey on graph anomaly detection with deep learning.TKDE, 35(12):12012–12038, 2021.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models A comprehensive survey on graph anomaly detection with deep learning.TKDE, 35(12):12012–12038, 2021
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.599965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a52c766e-1840-40c5-8cd8-953941e0ee40 · outbound
This paper cites Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks.arXiv, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks.arXiv, 2025
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.584040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9d770218-5bb9-4bb5-b521-e48d3224f64c · outbound
This paper cites Zero-shot generalist graph anomaly detection with unified neigh- borhood prompts.IJCAI, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Zero-shot generalist graph anomaly detection with unified neigh- borhood prompts.IJCAI, 2025
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.570779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 962dd7a8-d065-4f96-bc09-f077f8883cfa · outbound
This paper cites A label-free heterophily-guided approach for unsupervised graph fraud detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models A label-free heterophily-guided approach for unsupervised graph fraud detection
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.478587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f1af6761-2368-461b-a7c4-ca2750ba0ca3 · outbound
This paper cites Integrating graphs with large language models: Methods and prospects.IEEE Intelligent Systems, 39(1):64–68, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Integrating graphs with large language models: Methods and prospects.IEEE Intelligent Systems, 39(1):64–68, 2024
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.465004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 80e0ab0d-7d28-434c-a3ee-f6423a12fad1 · outbound
This paper cites Graph anomaly detection via adap- tive test-time representation learning across out-of-distribution domains.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Graph anomaly detection via adap- tive test-time representation learning across out-of-distribution domains
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.451465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 107d844a-4b07-44a7-b78e-99e09fa90fdd · outbound
This paper cites Anomalygfm: Graph foundation model for zero/few-shot anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Anomalygfm: Graph foundation model for zero/few-shot anomaly detection
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.437410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 61035334-605b-48c7-9eb1-d8972675ad5e · outbound
This paper cites Deep graph anomaly detection: A survey and new perspectives.TKDE, 37(9):5106–5126, 2025.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Deep graph anomaly detection: A survey and new perspectives.TKDE, 37(9):5106–5126, 2025
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.342803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 68e78d98-06d6-4bd0-a94c-295a60a4fc58 · outbound
This paper cites Heterophilic graph invariant learning for out-of-distribution of fraud detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Heterophilic graph invariant learning for out-of-distribution of fraud detection
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.312881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7957bccd-468e-4fce-b172-43b5026d05bc · outbound
This paper cites Understanding the information propagation effects of communication topologies in llm-based multi-agent systems.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Understanding the information propagation effects of communication topologies in llm-based multi-agent systems
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.298725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3accecb7-f280-4bf7-ad9c-c2bfc3a8ce4b · outbound
This paper cites Uniform: Towards unified framework for anomaly detection on graphs.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Uniform: Towards unified framework for anomaly detection on graphs
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.215841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 58b12900-4594-49a3-8511-2d611576b686 · outbound
This paper cites Anomaly subgraph detection through high-order sampling contrastive learning.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Anomaly subgraph detection through high-order sampling contrastive learning
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.061176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 51586e25-54cf-4abc-af4a-a4704b220a15 · outbound
This paper cites Gadbench: Revisiting and benchmarking supervised graph anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Gadbench: Revisiting and benchmarking supervised graph anomaly detection
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.048105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f2e999db-4c78-4c3a-b0f1-0c507d60f1f7 · outbound
This paper cites Goodat: towards test-time graph out-of- distribution detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Goodat: towards test-time graph out-of- distribution detection
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:24.034035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3dca1312-c71a-4f9d-9447-bf5d88da36d6 · outbound
This paper cites Cross-domain graph anomaly detection via anomaly-aware contrastive alignment.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Cross-domain graph anomaly detection via anomaly-aware contrastive alignment
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.987652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fd62945d-b330-44cf-b6fa-bc4ee66469cb · outbound
This paper cites Open-set graph anomaly detection via normal structure regularisation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Open-set graph anomaly detection via normal structure regularisation
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.829306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a1facd0d-22cc-41f8-851c-76ef31f1f38a · outbound
This paper cites Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.781267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 27ba43bc-1783-49de-84ba-d4d5e5ea4c37 · outbound
This paper cites Graph learning under distribution shifts: A comprehensive survey on domain adaptation, out-of-distribution, and continual learning.arXiv, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Graph learning under distribution shifts: A comprehensive survey on domain adaptation, out-of-distribution, and continual learning.arXiv, 2024
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.768800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 007fd32e-b7fb-421a-aeb0-32888e923daf · outbound
This paper cites Dynamic weighted learning for unsupervised domain adaptation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Dynamic weighted learning for unsupervised domain adaptation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.723195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cdf7897a-137e-44ce-9de0-be9d156fd0b3 · outbound
This paper cites Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Relation-aware graph convolutional networks for agent-initiated social e-commerce recommendation
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.636845Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c4400bd3-6293-457f-aa98-a41caac402b6 · outbound
This paper cites Metagad: Meta representation adaptation for few-shot graph anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Metagad: Meta representation adaptation for few-shot graph anomaly detection
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.580605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 80851796-02ac-41b4-9ae4-66ab1359f7e3 · outbound
This paper cites Ad- agent: A multi-agent framework for end-to-end anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Ad- agent: A multi-agent framework for end-to-end anomaly detection
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.563863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2ba46e3b-0213-441e-8a13-35ac51945507 · outbound
This paper cites Gram: An interpretable approach for graph anomaly detection using gradient attention maps.Neural Networks, 178, 2024.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Gram: An interpretable approach for graph anomaly detection using gradient attention maps.Neural Networks, 178, 2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.546392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fe630df1-c3ed-4a62-b1e7-8aba76663563 · outbound
This paper cites Freegad: A training-free yet effective approach for graph anomaly detection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Freegad: A training-free yet effective approach for graph anomaly detection
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.527335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c1cdd882-b23c-4878-9940-657e7dbb6403 · outbound
This paper cites Test- time graph neural dataset search with generative projection.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Test- time graph neural dataset search with generative projection
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.376390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4e029cf7-215d-4387-9a4b-073a8a98f457 · outbound
This paper cites Online gnn evaluation under test-time graph distribution shifts.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Online gnn evaluation under test-time graph distribution shifts
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.354851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 42edecf8-6308-4dad-a56a-06b3a0ff6bf5 · outbound
This paper cites Training- free graph anomaly detection: A simple approach via singular value decomposition.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Training- free graph anomaly detection: A simple approach via singular value decomposition
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.324120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1b420630-d000-4a61-94af-9dadfa932453 · outbound
This paper cites Domain generalization: A survey.TPAMI, 45(4):4396–4415, 2022.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Domain generalization: A survey.TPAMI, 45(4):4396–4415, 2022
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.212581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation dd48e426-9a35-45bf-9c6d-9788827089d1 · outbound
This paper cites Improving generalizability of graph anomaly detection models via data augmentation.TKDE, 35(12):12721–12735, 2023.
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models Improving generalizability of graph anomaly detection models via data augmentation.TKDE, 35(12):12721–12735, 2023
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T16:18:23.130339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
Observation f1589428-8ab1-469a-b59d-a2c29d3cccd8 · inbound
ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes cites this paper.
ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
Reference 34
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-12T19:12:02.447638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.