Paper Citation Record · LEDGER

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2501.14197.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.14197 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:18:52.891579Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee0c3ab5-9495-4f83-822e-9116b15577f1 · outbound

This paper cites Can abnormality be detected by graph neural networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI),.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Can abnormality be detected by graph neural networks? In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI),

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.322482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 841f9709-950d-494d-bc34-a54b5c39e0c8 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Semi-Supervised Classification with Graph Convolutional Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:52.813635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242a9c0e-12da-45b0-9ac3-179fc71a96b5 · outbound

This paper cites Sheng, Hui Xiong, and Le- man Akoglu.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Sheng, Hui Xiong, and Le- man Akoglu

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.169040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f198e80c-93c6-4f44-99b7-8c71d38cc789 · outbound

This paper cites Weidele Daniel, Bellei Claudio, Robinson Tom, and E.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Weidele Daniel, Bellei Claudio, Robinson Tom, and E

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.151658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5388813d-b553-49ce-aa21-ba5e2805bfc7 · outbound

This paper cites Deep Graph Anomaly Detection: A Survey and New Perspectives.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Deep Graph Anomaly Detection: A Survey and New Perspectives

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:52.843323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a7a78fc-c409-4715-86b2-835a7c1431fb · outbound

This paper cites Rethinking graph neural networks for anomaly de- tection.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Rethinking graph neural networks for anomaly de- tection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.081914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7eb3f585-5d4b-4fc2-bf67-2653c079c4ae · outbound

This paper cites Graph attention networks.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Graph attention networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:52.865712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6118c17-0e04-4125-ac9e-09b0486cbddf · outbound

This paper cites [Wang et al., 2025] Conghao Wang, Gaurav Asok Kumar, and Jagath C.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity [Wang et al., 2025] Conghao Wang, Gaurav Asok Kumar, and Jagath C

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.035710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c44ceddb-f0ae-478d-8a7e-7b80e61fb878 · outbound

This paper cites Clnode: Cur- riculum learning for node classification.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Clnode: Cur- riculum learning for node classification

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.016881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5ecace0-b365-45d5-89f8-2c1cbb5ff312 · outbound

This paper cites Splitgnn: Spectral graph neural network for fraud detection against heterophily.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Splitgnn: Spectral graph neural network for fraud detection against heterophily

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:52.999346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e1d74ca0-c5e0-48f8-a917-cd76f9ed9cac · outbound

This paper cites Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:18:52.941646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6a5553cc-d016-4120-a999-6335f5f209f3 · outbound

This paper cites Predicting dynamic embedding trajectory in temporal interaction networks.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Predicting dynamic embedding trajectory in temporal interaction networks

Reference 1958

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.098751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 168ab8b2-b44f-4a25-8ace-fc93c9a26a32 · outbound

This paper cites The perceptron: a probabilistic model for information storage and organiza- tion in the brain.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity The perceptron: a probabilistic model for information storage and organiza- tion in the brain

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.116364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d279060d-5a64-41d8-95a2-df73842b502c · outbound

This paper cites A universal adaptive algorithm for graph anomaly detection.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity A universal adaptive algorithm for graph anomaly detection

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.202221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1bfb3276-25be-4665-9e73-c4a726073ff2 · outbound

This paper cites F, and Singaraju Srinivasulu.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity F, and Singaraju Srinivasulu

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.219073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 37908ccb-ae80-46bc-a307-a9c664a0fa21 · outbound

This paper cites Curgraph: Curriculum learning for graph classification.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Curgraph: Curriculum learning for graph classification

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.053785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2c3cb812-7c92-435c-bf19-663e7f4c6ffe · outbound

This paper cites Ad- dressing heterophily in graph anomaly detection: A per- spective of graph spectrum.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Ad- dressing heterophily in graph anomaly detection: A per- spective of graph spectrum

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.253958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d8d7058-bab7-4e1f-8f0b-3e12a6690d43 · outbound

This paper cites Elhadad, Kin Fun Li, and Fayez Gebali.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Elhadad, Kin Fun Li, and Fayez Gebali

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.270444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 291a18ed-9cc6-4746-b4fa-fd0e1fefc756 · outbound

This paper cites Enhancing graph neural network-based fraud detectors against camouflaged fraudsters.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Enhancing graph neural network-based fraud detectors against camouflaged fraudsters

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.286949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb788e36-4cc5-416c-b777-771befbb734a · outbound

This paper cites Cuco: Graph representation with cur- riculum contrastive learning.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Cuco: Graph representation with cur- riculum contrastive learning

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.305445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99b5cc91-3808-4602-8dbf-e79c41bc10ce · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Hamilton, Rex Ying, and Jure Leskovec

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.236810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7c884a50-a4dc-48ab-bbae-be8ebcb682da · outbound

This paper cites Collective opinion spam detection: Bridging re- view networks and metadata.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Collective opinion spam detection: Bridging re- view networks and metadata

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.134315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dbab0a90-f243-4650-a1c8-683432335627 · outbound

This paper cites Pick and choose: A gnn-based imbalanced learning approach for fraud detection.

Bi-directional Curriculum Learning for Graph Anomaly Detection: Dual Focus on Homogeneity and Heterogeneity Pick and choose: A gnn-based imbalanced learning approach for fraud detection

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:18:53.184838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.