Paper Citation Record · LEDGER

MLDGG: Meta-Learning for Domain Generalization on Graphs

As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 1 inbound Pith citation observation for arXiv:2411.12913.

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

pith.paper-citation-record.v1
2411.12913 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:09:31.147223Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:14:46.010862Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T05:14:46.506829Z

Reference resolution

66 of 66 outbound references displayed

verified exact2
verified fuzzy40
unresolved24
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c9fc6a59-8ab6-4739-8f63-307273114b67 · outbound

This paper cites Domain generalization via invariant feature representation.

MLDGG: Meta-Learning for Domain Generalization on Graphs Domain generalization via invariant feature representation

Reference 1

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Source-reported events for the cited work

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Observation a5817106-ad78-4baf-a6c4-29f521b35cc7 · outbound

This paper cites Domain generalization with adversarial feature learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Domain generalization with adversarial feature learning

Reference 2

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Observation 2db873ba-e03b-4dc6-81b5-b299e66fa352 · outbound

This paper cites Supervised Algorithmic Fairness in Distribution Shifts: A Survey.

MLDGG: Meta-Learning for Domain Generalization on Graphs Supervised Algorithmic Fairness in Distribution Shifts: A Survey

Reference 3

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Source-reported events for the cited work

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Observation 94f2d92f-e3d1-4bc6-a18c-c97c1bedc813 · outbound

This paper cites Algorithmic fairness generalization under covariate and dependence shifts simultaneously.

MLDGG: Meta-Learning for Domain Generalization on Graphs Algorithmic fairness generalization under covariate and dependence shifts simultaneously

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.476180Z

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Observation 628ead41-84eb-4b52-8b75-8a2b6203fc11 · outbound

This paper cites Dynamic environment responsive online meta-learning with fairness awareness.

MLDGG: Meta-Learning for Domain Generalization on Graphs Dynamic environment responsive online meta-learning with fairness awareness

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.456309Z

Source-reported events for the cited work

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Observation dd8105e8-31ad-4f44-b061-845f1016b0a4 · outbound

This paper cites Learning fair invariant representations under covariate and correlation shifts simultaneously.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning fair invariant representations under covariate and correlation shifts simultaneously

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.434895Z

Source-reported events for the cited work

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Observation ad71e63e-c021-4058-9e87-fe69206f885f · outbound

This paper cites Towards counterfactual fairness-aware domain generalization in changing environments.

MLDGG: Meta-Learning for Domain Generalization on Graphs Towards counterfactual fairness-aware domain generalization in changing environments

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.413611Z

Source-reported events for the cited work

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Observation 3e4c7805-f5fb-41cf-ab37-d9794fb4026b · outbound

This paper cites Adaptation speed analysis for fairness- aware causal models.

MLDGG: Meta-Learning for Domain Generalization on Graphs Adaptation speed analysis for fairness- aware causal models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.392773Z

Source-reported events for the cited work

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Observation 2e9f6d23-a6ba-4b57-a558-493f15175bfe · outbound

This paper cites Towards fair disentangled online learning for changing environments.

MLDGG: Meta-Learning for Domain Generalization on Graphs Towards fair disentangled online learning for changing environments

Reference 9

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Observation b5f18810-13f9-4f63-aafc-959e1392d0d3 · outbound

This paper cites Adaptive fairness-aware online meta-learning for changing environments.

MLDGG: Meta-Learning for Domain Generalization on Graphs Adaptive fairness-aware online meta-learning for changing environments

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.355465Z

Source-reported events for the cited work

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Observation d8c4828a-9ab5-4f05-9093-9b007b3b37d7 · outbound

This paper cites Fairness-aware online meta-learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Fairness-aware online meta-learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.334489Z

Source-reported events for the cited work

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Observation 6d62f39e-090b-49c4-b543-822856e64e59 · outbound

This paper cites FEED: Fairness-Enhanced Meta-Learning for Domain Generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs FEED: Fairness-Enhanced Meta-Learning for Domain Generalization

Reference 12

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Source-reported events for the cited work

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Observation 9e7dd291-a4c4-4aa8-9a6e-9d27ad8750e3 · outbound

This paper cites Domain generalization using causal matching.

MLDGG: Meta-Learning for Domain Generalization on Graphs Domain generalization using causal matching

Reference 13

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Observation 0b9d05e1-6d83-4f0f-a6fb-85688b29a50c · outbound

This paper cites Causality inspired representation learning for domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Causality inspired representation learning for domain generalization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.302720Z

Source-reported events for the cited work

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Observation 21803842-a377-4917-bb27-9eba57a5a720 · outbound

This paper cites Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs.

MLDGG: Meta-Learning for Domain Generalization on Graphs Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.282773Z

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Observation 552af976-079e-4ece-9881-2500cf2ff691 · outbound

This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

MLDGG: Meta-Learning for Domain Generalization on Graphs Handling Distribution Shifts on Graphs: An Invariance Perspective

Reference 16

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Source-reported events for the cited work

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Observation 6298b6ba-2565-4812-af30-ab7996c7d025 · outbound

This paper cites Metropolis-hastings data augmentation for graph neural networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs Metropolis-hastings data augmentation for graph neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.263791Z

Source-reported events for the cited work

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Observation 82ef4a69-0676-4719-abfa-bb8faa690e2c · outbound

This paper cites Robust optimization as data augmentation for large-scale graphs.

MLDGG: Meta-Learning for Domain Generalization on Graphs Robust optimization as data augmentation for large-scale graphs

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.243620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8229261-b503-472b-9194-55b1550e7597 · outbound

This paper cites Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift.

MLDGG: Meta-Learning for Domain Generalization on Graphs Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

Reference 19

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Source-reported events for the cited work

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Observation 778abec8-105b-49f5-bffc-076d2c765707 · outbound

This paper cites Learning to learn with variational information bottleneck for domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning to learn with variational information bottleneck for domain generalization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.223043Z

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Observation c44cecce-8a9f-4649-b723-3d9dce32403d · outbound

This paper cites Causalvae: Disentangled representation learning via neural structural causal models.

MLDGG: Meta-Learning for Domain Generalization on Graphs Causalvae: Disentangled representation learning via neural structural causal models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.205936Z

Source-reported events for the cited work

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Observation c579517c-d83d-4525-b0f5-f318a0fad10c · outbound

This paper cites Causal attention for interpretable and generalizable graph classification.

MLDGG: Meta-Learning for Domain Generalization on Graphs Causal attention for interpretable and generalizable graph classification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.188280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 387b4481-4f08-4f47-80ba-12c2ca95a009 · outbound

This paper cites Learning invariant graph representations for out-of- distribution generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning invariant graph representations for out-of- distribution generalization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.167129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f36aecf-ad70-4dc4-97d4-b94b5644cb98 · outbound

This paper cites Energy-based out-of-distribution detection for graph neural networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs Energy-based out-of-distribution detection for graph neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.147926Z

Source-reported events for the cited work

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Observation 01dcec45-c721-4ebf-9245-6e14b4a27158 · outbound

This paper cites Multi-scale attributed node embedding.

MLDGG: Meta-Learning for Domain Generalization on Graphs Multi-scale attributed node embedding

Reference 25

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Observation 97dca0b3-7ff1-483d-b965-c725a56e18e9 · outbound

This paper cites Social structure of facebook networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs Social structure of facebook networks

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b32c955-7852-45cf-ba8a-d4704b5de1ae · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs Geom-GCN: Geometric Graph Convolutional Networks

Reference 27

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Observation f53f653a-fd54-4d8b-96ca-3b10366bbb9b · outbound

This paper cites GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:09:31.294963Z

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Observation a50e2217-2c92-4bfd-b125-3f2a00b9c839 · outbound

This paper cites Model-based domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Model-based domain generalization

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2659ba9e-9872-4628-bc55-99e431304b49 · outbound

This paper cites Learn to expect the unexpected: Probably approximately correct domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learn to expect the unexpected: Probably approximately correct domain generalization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.082215Z

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Observation 700a6714-c182-4b66-bd01-f4b2174cd107 · outbound

This paper cites Domain generalization: A survey.

MLDGG: Meta-Learning for Domain Generalization on Graphs Domain generalization: A survey

Reference 31

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Observation 9d40c932-e81a-4cd2-954b-153353e5afdd · outbound

This paper cites Robust optimization over multiple domains.

MLDGG: Meta-Learning for Domain Generalization on Graphs Robust optimization over multiple domains

Reference 32

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Observation 32448b5d-7f19-4102-85d4-daa734862943 · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 33

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Observation 9d40a484-efe0-4391-b1ad-3e8b18a7c29f · outbound

This paper cites Out-of-distribution generalization via risk extrapolation (rex).

MLDGG: Meta-Learning for Domain Generalization on Graphs Out-of-distribution generalization via risk extrapolation (rex)

Reference 34

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Source-reported events for the cited work

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Observation 7b904839-a25f-40f6-8ef9-6e2cbd6d77a7 · outbound

This paper cites Learning to generalize: Meta-learning for domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning to generalize: Meta-learning for domain generalization

Reference 35

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Source-reported events for the cited work

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Observation afbe4a9d-c507-4968-b88d-f0ebca7d464c · outbound

This paper cites Discriminative adversarial domain generalization with meta-learning based cross-domain validation.

MLDGG: Meta-Learning for Domain Generalization on Graphs Discriminative adversarial domain generalization with meta-learning based cross-domain validation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:32.007649Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 427be59a-f110-45be-8761-988369c84f6c · outbound

This paper cites Cross-domain few-shot graph classification.

MLDGG: Meta-Learning for Domain Generalization on Graphs Cross-domain few-shot graph classification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.990390Z

Source-reported events for the cited work

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Observation 367b69a2-7220-47ea-b7db-a7bbefc5e518 · outbound

This paper cites Adapting distilled knowledge for few-shot relation reasoning over knowledge graphs.

MLDGG: Meta-Learning for Domain Generalization on Graphs Adapting distilled knowledge for few-shot relation reasoning over knowledge graphs

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.972246Z

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Observation 4855d69a-6328-4a23-80f5-d6c0d22eb760 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

MLDGG: Meta-Learning for Domain Generalization on Graphs Model-agnostic meta-learning for fast adaptation of deep networks

Reference 39

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Observation 6fe58146-3cb4-48e4-b639-6c91cd3d17cf · outbound

This paper cites A perspective view and survey of meta-learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs A perspective view and survey of meta-learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.940511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f817893-d6c5-46f6-ba7d-dad383737ac0 · outbound

This paper cites Episodic training for domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Episodic training for domain generalization

Reference 41

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Source-reported events for the cited work

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Observation 5bb87702-f899-42de-ae45-0447d06c6481 · outbound

This paper cites Metareg: Towards domain generalization using meta-regularization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Metareg: Towards domain generalization using meta-regularization

Reference 42

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Source-reported events for the cited work

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Observation a7e74d41-3a71-4762-8f1b-2f0552653101 · outbound

This paper cites Domain generalization via model-agnostic learning of semantic features.

MLDGG: Meta-Learning for Domain Generalization on Graphs Domain generalization via model-agnostic learning of semantic features

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.897740Z

Source-reported events for the cited work

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Observation 4c53f712-234f-4283-8b45-b4d12f752b50 · outbound

This paper cites Graph meta learning via local subgraphs.

MLDGG: Meta-Learning for Domain Generalization on Graphs Graph meta learning via local subgraphs

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.879075Z

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Observation c87174ba-27b1-42fc-b7ea-5c657e1abe31 · outbound

This paper cites Meta-gnn: On few- shot node classification in graph meta-learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Meta-gnn: On few- shot node classification in graph meta-learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.858763Z

Source-reported events for the cited work

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Observation c3307dd3-b569-40fd-8b15-386587d01413 · outbound

This paper cites Graph few-shot learning via knowledge transfer.

MLDGG: Meta-Learning for Domain Generalization on Graphs Graph few-shot learning via knowledge transfer

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.838836Z

Source-reported events for the cited work

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Observation d0981b91-3518-450f-8e7d-a187855bbaf0 · outbound

This paper cites Graph few-shot learning with attribute matching.

MLDGG: Meta-Learning for Domain Generalization on Graphs Graph few-shot learning with attribute matching

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.819191Z

Source-reported events for the cited work

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Observation 07127047-6a12-4ac9-8790-dfe59bd831ef · outbound

This paper cites Adaptive-step graph meta-learner for few-shot graph classification.

MLDGG: Meta-Learning for Domain Generalization on Graphs Adaptive-step graph meta-learner for few-shot graph classification

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.797908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30aea34e-b928-4b3c-8717-e9875551d4a5 · outbound

This paper cites Few-shot graph learning for molecular property prediction.

MLDGG: Meta-Learning for Domain Generalization on Graphs Few-shot graph learning for molecular property prediction

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.779101Z

Source-reported events for the cited work

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Observation aba5449a-9069-4d1c-98ce-0dd85df703c7 · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning to compare: Relation network for few-shot learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.050831Z

Source-reported events for the cited work

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Observation c53b8ad2-811b-4623-879e-f8ef79cb8365 · outbound

This paper cites Graph few-shot class-incremental learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Graph few-shot class-incremental learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.747752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae614013-e084-4afc-9c61-29175464aa78 · outbound

This paper cites Probabilistic model-agnostic meta-learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Probabilistic model-agnostic meta-learning

Reference 52

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Observation be0d8628-5a6a-4211-9597-23919429c0bb · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Generalizing from a few examples: A survey on few-shot learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.072517Z

Source-reported events for the cited work

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Observation fecaed5c-3e6e-497a-b7b5-6e7c6258ef46 · outbound

This paper cites Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-.

MLDGG: Meta-Learning for Domain Generalization on Graphs Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.078437Z

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Observation 59d4de02-3f3f-4797-a10b-d270fa836773 · outbound

This paper cites Learning to learn single domain generalization.

MLDGG: Meta-Learning for Domain Generalization on Graphs Learning to learn single domain generalization

Reference 55

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This paper cites How to train your maml.

MLDGG: Meta-Learning for Domain Generalization on Graphs How to train your maml

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.553728Z

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This paper cites Multi-domain generalized graph meta learning.

MLDGG: Meta-Learning for Domain Generalization on Graphs Multi-domain generalized graph meta learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.534267Z

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Observation cf03a731-6417-4e96-a3f9-7bc69d986092 · outbound

This paper cites Sample efficient reinforcement learning with reinforce.

MLDGG: Meta-Learning for Domain Generalization on Graphs Sample efficient reinforcement learning with reinforce

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.515295Z

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Observation 2ea9043c-7b68-4190-a29d-a8ec4e289861 · outbound

This paper cites Auto-Encoding Variational Bayes.

MLDGG: Meta-Learning for Domain Generalization on Graphs Auto-Encoding Variational Bayes

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.105844Z

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This paper cites An introduction to variational methods for graphical models.

MLDGG: Meta-Learning for Domain Generalization on Graphs An introduction to variational methods for graphical models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.495344Z

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Observation 77b7689f-e7e6-4084-b773-2456654374f2 · outbound

This paper cites Analysis of the cholesky decomposition of a semi-definite matrix.

MLDGG: Meta-Learning for Domain Generalization on Graphs Analysis of the cholesky decomposition of a semi-definite matrix

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.477225Z

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Observation f15d3089-cb94-4098-b352-90fcc6b4b156 · outbound

This paper cites A new metric for probability distributions.

MLDGG: Meta-Learning for Domain Generalization on Graphs A new metric for probability distributions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.458831Z

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Observation 2d0a95fc-04f4-44d4-b1fd-0ea576ad33d1 · outbound

This paper cites Shift-robust gnns: Overcoming the limitations of localized graph training data.

MLDGG: Meta-Learning for Domain Generalization on Graphs Shift-robust gnns: Overcoming the limitations of localized graph training data

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.438962Z

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Observation 68b5a4b8-572c-476e-b09f-734bacc62c80 · outbound

This paper cites Mixup for node and graph classification.

MLDGG: Meta-Learning for Domain Generalization on Graphs Mixup for node and graph classification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:09:31.419534Z

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Observation cd3e31d5-49d2-4366-ad84-931c6e59d552 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

MLDGG: Meta-Learning for Domain Generalization on Graphs mixup: Beyond Empirical Risk Minimization

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.141356Z

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Observation 05fb9ad5-a3ec-40e6-9a9b-3deda260f279 · outbound

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

MLDGG: Meta-Learning for Domain Generalization on Graphs Semi-Supervised Classification with Graph Convolutional Networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T17:09:31.147223Z

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Pith citing papers

Observation 76c0515f-8644-4aa0-9b32-74720df83d1e · inbound

Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation cites this paper.

Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation MLDGG: Meta-Learning for Domain Generalization on Graphs

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:14:46.510810Z

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