Paper Citation Record · LEDGER

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?

As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2501.02766.

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

pith.paper-citation-record.v1
2501.02766 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:09:10.883532Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy23
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c65015b-acf6-4960-a710-00be9c5a78f5 · outbound

This paper cites an unresolved cited work.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:09:11.368004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa96ded9-9deb-49cc-84e3-feaa54dcc091 · outbound

This paper cites Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-Source Data,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-Source Data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.351280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c242b50b-2301-4f87-9fa8-0d2a192f7cf0 · outbound

This paper cites Interpretable Failure Localization for Microservice Sys- tems Based on Graph Autoencoder,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Interpretable Failure Localization for Microservice Sys- tems Based on Graph Autoencoder,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.335019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5d3cbea2-1b5c-4dc3-9327-99468032e7c9 · outbound

This paper cites Fault-Aware Service Scheduling Optimization Frame- work in Edge Data Center,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Fault-Aware Service Scheduling Optimization Frame- work in Edge Data Center,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.317353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5b0c46d0-ae8f-42fa-a4fe-f01e788cde6d · outbound

This paper cites TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework with Multimodal Data.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework with Multimodal Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:09:10.760494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58fb6167-e6de-4421-8e91-b09b8e4c821a · outbound

This paper cites Robust Failure Diagnosis of Microservice System Through Multimodal Data,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Robust Failure Diagnosis of Microservice System Through Multimodal Data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.300530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2fab7a1-bb57-4513-898d-46d40971864c · outbound

This paper cites CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:09:10.772199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ffaf69f-22e2-4f7a-8e08-34ece6b83eb5 · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Graph neural networks: A review of methods and applications,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.284185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9aaa510d-32b0-438f-bda5-a846f4eb1c33 · outbound

This paper cites DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? DeepTraLog: Trace-Log Combined Microservice Anomaly Detection through Graph-based Deep Learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.267429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a97bcc65-5daf-47e0-a92c-24fd478bca27 · outbound

This paper cites Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microser- vice System,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microser- vice System,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.250954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f022aa1b-8463-4fcb-a0f0-3dffab762b4c · outbound

This paper cites Drain: An Online Log Parsing Approach with Fixed Depth Tree,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Drain: An Online Log Parsing Approach with Fixed Depth Tree,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.234226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32a8f0ec-c804-4ee0-a65d-797585af8b26 · outbound

This paper cites MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.216698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0e4f37c-383b-40a3-bdb0-15760f6727f2 · outbound

This paper cites Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Nezha: Interpretable Fine-Grained Root Causes Analysis for Microservices on Multi-modal Observability Data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.200483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1c0a0a1-6780-4d31-8a59-3516388cb2e6 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:09:10.808831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b7a9958-3482-4982-8460-b77ac1fcc338 · outbound

This paper cites Attention is All you Need,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Attention is All you Need,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.185221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 463e814e-d52e-42a2-8b6d-ad270e124696 · outbound

This paper cites Enriching Word Vectors with Subword Information,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Enriching Word Vectors with Subword Information,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.169072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b555a45-7f20-419b-88ca-93df3ed45cb4 · outbound

This paper cites Glove: Global vectors for word representation,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Glove: Global vectors for word representation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.153638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 73d9bdbd-48dd-4b3b-91d1-947cebc06ccd · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.137809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 129ada10-0749-43b4-83dd-030aaea7a478 · outbound

This paper cites DGERCL: A Dynamic Graph Embedding Approach for Root Cause Localization in Microser- vice Systems,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? DGERCL: A Dynamic Graph Embedding Approach for Root Cause Localization in Microser- vice Systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.120377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4f3e43a-dfb6-4168-a782-ca589818dfa8 · outbound

This paper cites Deep sets,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Deep sets,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.103610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23f2b941-273b-4fa1-b0ca-f0ab25ef8fed · outbound

This paper cites Set transformer: A framework for attention-based permutation-invariant neu- ral networks,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Set transformer: A framework for attention-based permutation-invariant neu- ral networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.087121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e4a5587a-ae94-4b4c-9832-83c565ae831b · outbound

This paper cites Characterizing Microservice Dependency and Perfor- mance: Alibaba Trace Analysis,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Characterizing Microservice Dependency and Perfor- mance: Alibaba Trace Analysis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.070980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f10c1e48-e7de-473f-84d9-807724f5d3f9 · outbound

This paper cites CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.054042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 758aa8be-6a78-4d11-b6f3-fccc9fc67ba7 · outbound

This paper cites Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T22:09:10.862113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03e55868-5cd7-402b-9fcb-ad2ed74c3b63 · outbound

This paper cites Graph Neural Networks with Learnable Structural and Positional Representa- tions,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Graph Neural Networks with Learnable Structural and Positional Representa- tions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.038097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1005749e-dba2-4ec7-9559-5bbd20c5e09d · outbound

This paper cites Inductive Representation Learning on Large Graphs,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Inductive Representation Learning on Large Graphs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.021570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8535f55-84b7-428e-b2ac-c9014ef4a34c · outbound

This paper cites Graph Attention Networks,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? Graph Attention Networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:11.005938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e460a943-cab1-4864-868c-67dc537eb42a · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection,.

Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems? UMAP: Uniform Manifold Approximation and Projection,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:09:10.989467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.