MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL | Paper Citation Record

Observation a4df76f0-9377-4a53-9993-27f7f894a327 · outbound

This paper cites The graph neural network model,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL The graph neural network model,

Reference 1

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Observation 32c8b787-c9c5-4b85-97af-a3a1d4a127ed · outbound

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

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Semi-Supervised Classification with Graph Convolutional Networks

Reference 2

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Observation 1021f213-cc44-40a6-8279-a9a84acf4868 · outbound

This paper cites Inductive representation learning on large graphs,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Inductive representation learning on large graphs,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:30.079438Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa757464-6543-4b69-8dca-80448d10a654 · outbound

This paper cites Graph Neural Networks with Continual Learning for Fake News Detection from Social Media.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph Neural Networks with Continual Learning for Fake News Detection from Social Media

Reference 4

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Observation 404b89a5-2be1-4032-a536-aeba0c682fb7 · outbound

This paper cites Graduate employment prediction with bias,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graduate employment prediction with bias,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:30.061056Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ce54c24e-2de9-4248-a8ce-d78c9ab7c0f2 · outbound

This paper cites Predicting multicellular function through multi-layer tissue networks,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Predicting multicellular function through multi-layer tissue networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:30.044884Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6888abe3-489e-4dae-aab9-d8850c9e8321 · outbound

This paper cites Protein interface prediction using graph convolutional networks,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Protein interface prediction using graph convolutional networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:30.027331Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 688e752b-9d3a-4411-a9ac-4f38a82f7bce · outbound

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

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Meta- gnn: On few-shot node classification in graph meta-learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:30.008435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ebfab9e5-f130-4288-abbd-7d6f9da382ff · outbound

This paper cites Graph proto- typical networks for few-shot learning on attributed networks,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph proto- typical networks for few-shot learning on attributed networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.990881Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f023aa6-a901-49bd-ba77-10271632c1ad · outbound

This paper cites Meta-learning with graph neural networks: Methods and applications,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Meta-learning with graph neural networks: Methods and applications,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.974129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38ac9482-3016-4257-8d90-c9ad49426f01 · outbound

This paper cites Meta-inductive node classification across graphs,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Meta-inductive node classification across graphs,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.957637Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation efd9e648-116e-4998-ae56-3faf2398bb09 · outbound

This paper cites Graph lifelong learning: A survey,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph lifelong learning: A survey,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.938495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 32b4005a-d214-4f7c-aaef-baef42dcd939 · outbound

This paper cites Self-supervised continual graph learning in adaptive riemannian spaces,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Self-supervised continual graph learning in adaptive riemannian spaces,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.919980Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fc724f00-0b49-4cb5-8ff1-016c937cb666 · outbound

This paper cites Cat: Balanced continual graph learning with graph condensation,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Cat: Balanced continual graph learning with graph condensation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.902317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 49ec5052-a1db-4ace-91b1-ca208bc83b03 · outbound

This paper cites Continual learning on dynamic graphs via parameter isolation,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Continual learning on dynamic graphs via parameter isolation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.885592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aceb47ba-1eb2-4ac6-82a8-b63174e428c0 · outbound

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

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph few-shot class-incremental learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.868740Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8f2280db-84d3-4c84-a60a-e8babe3f8327 · outbound

This paper cites Geometer: Graph few-shot class-incremental learning via prototype representation,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Geometer: Graph few-shot class-incremental learning via prototype representation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.850733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da7fecf9-3018-45ab-b2f0-6d23eedd6efb · outbound

This paper cites Prototypical networks for few-shot learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Prototypical networks for few-shot learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.830841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9bdb8b03-06fa-4102-b0e9-8255ec1bed42 · outbound

This paper cites Cglb: Benchmark tasks for continual graph learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Cglb: Benchmark tasks for continual graph learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.814405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f6f07e75-5dee-4c82-b7aa-0eee18f9eee8 · outbound

This paper cites Sparsified subgraph memory for continual graph representation learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Sparsified subgraph memory for continual graph representation learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.797360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c33192b0-8aa1-4bcc-89a3-14f527d028da · outbound

This paper cites Learning without forgetting,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Learning without forgetting,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.777484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1047be83-a3f2-4115-98a4-b3769df7dfd8 · outbound

This paper cites Incremental few-shot learning via vector quantization in deep embedded space,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Incremental few-shot learning via vector quantization in deep embedded space,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.761717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 002f67d9-e804-48a7-9bbf-bc15227640ea · outbound

This paper cites For- ward compatible few-shot class-incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL For- ward compatible few-shot class-incremental learning,

Reference 23

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Observation 66616acb-1635-4c01-b463-a56b3ad2f166 · outbound

This paper cites Semantics- driven generative replay for few-shot class incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Semantics- driven generative replay for few-shot class incremental learning,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:29.104427Z

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Observation 41bfa3b1-c515-4afa-9e13-c9a0dc3f3158 · outbound

This paper cites On the soft-subnetwork for few-shot class incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL On the soft-subnetwork for few-shot class incremental learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.719935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f192b090-84bc-4e2b-a0f3-b4fa2088731a · outbound

This paper cites A survey on few- shot class-incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL A survey on few- shot class-incremental learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.703073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bfdf94e5-5c8b-4cbd-80e8-2d53daaba940 · outbound

This paper cites Few-shot class-incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Few-shot class-incremental learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.686151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9971ae0c-745c-4a33-8184-bb0b36e85ca9 · outbound

This paper cites S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL S3c: Self-supervised stochastic classifiers for few-shot class-incremental learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.668820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c0a2af1-cc05-4e20-bd5d-03b0de3cbf3b · outbound

This paper cites A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:29.131073Z

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Observation bc924ed1-78ec-4e53-81ea-9c074d0ab66c · outbound

This paper cites Advances and challenges in meta-learning: A technical re- view,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Advances and challenges in meta-learning: A technical re- view,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.650889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1b94d79d-413b-4d9d-a3c9-8a3beb914e32 · outbound

This paper cites Meta-gps++: Enhancing graph meta-learning with contrastive learning and self-training,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Meta-gps++: Enhancing graph meta-learning with contrastive learning and self-training,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.634442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b89a64e-6cc0-409c-9b02-c303bde69f2e · outbound

This paper cites Relative and absolute location embedding for few-shot node classification on graph,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Relative and absolute location embedding for few-shot node classification on graph,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.617854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 239b6840-1fd8-4d9b-8723-1a265e48b63f · outbound

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

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph few-shot learning via knowledge transfer,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.601206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b0a3d35-7bc3-4c2b-9e92-b7a3d0fb85b5 · outbound

This paper cites Adaptive attentional network for few-shot knowledge graph completion,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Adaptive attentional network for few-shot knowledge graph completion,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.583538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26e9adb2-80f5-4670-bc32-7195a241202a · outbound

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

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 35

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Observation 966a223e-e5da-4c05-bd06-82e798466e94 · outbound

This paper cites Graph meta learning via local subgraphs,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph meta learning via local subgraphs,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.555911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f4e79c3e-c741-4a31-b68b-c985d537898b · outbound

This paper cites Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a877e12-994d-4b4a-9fea-c3129af17814 · outbound

This paper cites Lifelong learning via progressive distillation and retrospection,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Lifelong learning via progressive distillation and retrospection,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.539502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93800cb5-4320-4d10-a7b3-1ab83abd0fcf · outbound

This paper cites End-to-end incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL End-to-end incremental learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.522216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 084b4104-9f8e-4dd6-8e6a-a7d2d3ff19d2 · outbound

This paper cites Inferring networks of substi- tutable and complementary products,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Inferring networks of substi- tutable and complementary products,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.506320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2cf61645-dff5-4efc-96e0-d6150a704ad5 · outbound

This paper cites Arnetminer: extraction and mining of academic social networks,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Arnetminer: extraction and mining of academic social networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.490371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 60441290-6534-42a8-a957-f13a8d579900 · outbound

This paper cites Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Reference 42

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Observation 3c2be8ae-d8a9-47c4-8be5-23a3e69d1e42 · outbound

This paper cites Graph Attention Networks.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Graph Attention Networks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T12:07:29.202024Z

Source-reported events for the cited work

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Observation cab466b3-4478-4f96-a61c-f410003f5b61 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.472723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 445bb45b-de82-4f06-bc64-67967bdf99a4 · outbound

This paper cites Ftf-er: Feature-topology fusion-based experience replay method for continual graph learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Ftf-er: Feature-topology fusion-based experience replay method for continual graph learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.452820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc682351-4e66-4e9b-a0be-0b71e44b4eaf · outbound

This paper cites Memory aware synapses: Learning what (not) to forget,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Memory aware synapses: Learning what (not) to forget,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.436470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3e8bf7c-daad-4c4b-8ffe-b327bce10729 · outbound

This paper cites Overcoming catastrophic forgetting in graph neural networks,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Overcoming catastrophic forgetting in graph neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.419413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation faa0b730-184f-484b-afa2-5c0322f369b6 · outbound

This paper cites An efficient memory module for graph few-shot class-incremental learning,.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL An efficient memory module for graph few-shot class-incremental learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.402463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fb9c01f-7be9-4992-a699-13d7f9823ab6 · outbound

This paper cites Visualizing data using t-sne.

MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL Visualizing data using t-sne

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:07:29.385589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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