Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation | Paper Citation Record

Observation 046d191e-1645-47a3-a289-52dcb8f711d9 · outbound

This paper cites write newline.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation write newline

Reference 1

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This paper cites Invariant Risk Minimization.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Invariant Risk Minimization

Reference 2

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This paper cites M., Ong, C.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation M., Ong, C

Reference 3

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Observation a3ac9210-8e5a-492f-823d-ad53c37ff597 · outbound

This paper cites Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Reference 4

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Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Unresolved cited work

Reference 5

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This paper cites Graph-based prediction and planning policy network (gp3net) for scalable self-driving in dynamic environments using deep reinforcement learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Graph-based prediction and planning policy network (gp3net) for scalable self-driving in dynamic environments using deep reinforcement learning

Reference 6

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Observation b1b8d061-7313-4ef9-9b33-bc7524ffa705 · outbound

This paper cites Graph transfer learning via adversarial domain adaptation with graph convolution.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Graph transfer learning via adversarial domain adaptation with graph convolution

Reference 7

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Observation 19f70e7d-3968-4f61-99c2-d4c1bddc4272 · outbound

This paper cites Informative class-conditioned feature alignment for unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Informative class-conditioned feature alignment for unsupervised domain adaptation

Reference 8

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Observation 05492c88-f34c-4178-9858-8d25714a0eac · outbound

This paper cites Semi-supervised learning on graphs with generative adversarial nets.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Semi-supervised learning on graphs with generative adversarial nets

Reference 9

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Observation 754c0318-875a-4cd5-ad8a-76508fa7a9cc · outbound

This paper cites Domain-adversarial training of neural networks.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Domain-adversarial training of neural networks

Reference 10

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Observation d9f3a734-1b7c-438f-9a10-2002c21793de · outbound

This paper cites S., Riley, P.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation S., Riley, P

Reference 11

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Observation 8fe39000-45a0-4561-803a-1f22a3b6e365 · outbound

This paper cites Inductive representation learning on large graphs.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Inductive representation learning on large graphs

Reference 12

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Observation 0a67a1e0-77f4-44b9-9810-438c2dc37822 · outbound

This paper cites A., M \"u ller, K.-R., and Tkatchenko, A.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation A., M \"u ller, K.-R., and Tkatchenko, A

Reference 13

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Observation 9a989f4a-73e3-4e0b-8d3a-96f92a212eae · outbound

This paper cites Asgn: An active semi-supervised graph neural network for molecular property prediction.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Asgn: An active semi-supervised graph neural network for molecular property prediction

Reference 14

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This paper cites Momentum contrast for unsupervised visual representation learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Momentum contrast for unsupervised visual representation learning

Reference 15

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Observation 7978ff77-77e6-4281-9181-d324a2117583 · outbound

This paper cites Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification

Reference 16

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Observation e4d45420-6410-4308-857b-0a1fe9456085 · outbound

This paper cites D., Kramer, S., and Srinivasan, A.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation D., Kramer, S., and Srinivasan, A

Reference 17

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Observation afcf83be-3716-4480-9cd2-87e17b5f3fad · outbound

This paper cites and Zitnik, M.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Zitnik, M

Reference 18

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Observation b99063da-d9f1-4471-ac8b-5c5970d8487e · outbound

This paper cites Incomplete graph learning via attribute-structure decoupled variational auto-encoder.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Incomplete graph learning via attribute-structure decoupled variational auto-encoder

Reference 19

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Observation 57514213-34e3-4682-a9a4-74b6c1d161b5 · outbound

This paper cites Ragraph: A general retrieval-augmented graph learning framework.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Ragraph: A general retrieval-augmented graph learning framework

Reference 20

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This paper cites A survey of data-efficient graph learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation A survey of data-efficient graph learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:12.563317Z

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Observation df56871b-583c-40ac-9cfc-c08335cc2eac · outbound

This paper cites N., Rahnavard, N., Mian, A., and Shah, M.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation N., Rahnavard, N., Mian, A., and Shah, M

Reference 22

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Observation 7035ca92-e391-458b-a197-1372300ce8ab · outbound

This paper cites Learning topology-specific experts for molecular property prediction.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Learning topology-specific experts for molecular property prediction

Reference 23

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Observation 5edd67bb-f66f-4311-912e-b22315e769f2 · outbound

This paper cites kgcn: a graph-based deep learning framework for chemical structures.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation kgcn: a graph-based deep learning framework for chemical structures

Reference 24

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Observation 87596067-5341-4a1d-bc5b-79c9b01e6dbc · outbound

This paper cites Adversary for social good: Protecting familial privacy through joint adversarial attacks.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Adversary for social good: Protecting familial privacy through joint adversarial attacks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:11.465089Z

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Observation a723d857-a069-424c-a346-4ad16d9cc13f · outbound

This paper cites H., and Ulbricht, D.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation H., and Ulbricht, D

Reference 26

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Source-reported events for the cited work

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Observation 163d1f09-eb79-4b54-a05e-70c0eabbd4aa · outbound

This paper cites Self-attention graph pooling.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Self-attention graph pooling

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:10.913822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9cca9ad4-e49d-4093-b8b0-914de5938860 · outbound

This paper cites Relevance-aware anomalous users detection in social network via graph neural network.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Relevance-aware anomalous users detection in social network via graph neural network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:10.604831Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bba2ddd0-94e2-46ff-be9a-c676dda6f8fd · outbound

This paper cites Multi-domain generalized graph meta learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Multi-domain generalized graph meta learning

Reference 29

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Observation b585768b-5c27-4630-941a-3f919f39be42 · outbound

This paper cites Content matters: A gnn-based model combined with text semantics for social network cascade prediction.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Content matters: A gnn-based model combined with text semantics for social network cascade prediction

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:10.174831Z

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Observation 28c59239-5cec-4b15-9662-65198adb6915 · outbound

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Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Unresolved cited work

Reference 31

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Observation 3ff413c9-ff81-41e0-972a-b05526841d32 · outbound

This paper cites Graph Out-of-Distribution Generalization with Controllable Data Augmentation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Graph Out-of-Distribution Generalization with Controllable Data Augmentation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:00.969515Z

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Observation fa9f26a2-2c6d-4496-a0d5-a9808bbd3ef9 · outbound

This paper cites Gala: Graph diffusion-based alignment with jigsaw for source-free domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Gala: Graph diffusion-based alignment with jigsaw for source-free domain adaptation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:09.644891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 169b7165-5d1b-44c3-9d78-5532d68789e5 · outbound

This paper cites Rank and align: towards effective source-free graph domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Rank and align: towards effective source-free graph domain adaptation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:09.419511Z

Source-reported events for the cited work

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Observation 3c78f710-195f-47a5-ba70-9a268134648e · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 35

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Source-reported events for the cited work

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Observation 09083208-79e3-49e7-b81c-d68b25298e17 · outbound

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Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:42:09.188501Z

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Observation 615ab53a-f21e-4cb7-873f-4631985f6ba0 · outbound

This paper cites J., Micorek, J., Possegger, H., and Bischof, H.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation J., Micorek, J., Possegger, H., and Bischof, H

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.968010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f802ef6d-cfe1-47cd-acd1-9ec10655ffc4 · outbound

This paper cites Dare-gram: Unsupervised domain adaptation regression by aligning inverse gram matrices.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Dare-gram: Unsupervised domain adaptation regression by aligning inverse gram matrices

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.861336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation faa5812e-dbef-4664-bdd6-bd3dbd762820 · outbound

This paper cites Cogboost: Boosting for fast cost-sensitive graph classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Cogboost: Boosting for fast cost-sensitive graph classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.714853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a33c816d-62ff-4cfa-848e-80fc90a1d323 · outbound

This paper cites and Vural, E.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Vural, E

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.569158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 738a9551-0cfe-4586-ad15-a91887c9051c · outbound

This paper cites Asap: Adaptive structure aware pooling for learning hierarchical graph representations.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Asap: Adaptive structure aware pooling for learning hierarchical graph representations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.440746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6604aed3-8f25-47fe-a2f1-c327a48a48f6 · outbound

This paper cites and Bunke, H.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Bunke, H

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.305847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c52c844-66c3-4c03-8a7b-8b718b8f1de7 · outbound

This paper cites and Garg, S.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Garg, S

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.201677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0748f543-a45a-4434-a4b9-c88c1641a48b · outbound

This paper cites Maximum classifier discrepancy for unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Maximum classifier discrepancy for unsupervised domain adaptation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:08.040018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1f04f6a-e298-4201-aae6-480fbcb2bcf7 · outbound

This paper cites Adversarial deep network embedding for cross-network node classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Adversarial deep network embedding for cross-network node classification

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.906923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9c8cf90-214f-412e-a8c4-c15c290034d6 · outbound

This paper cites Pairwise adversarial training for unsupervised class-imbalanced domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Pairwise adversarial training for unsupervised class-imbalanced domain adaptation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.789165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd7f7a0a-ca23-4241-bab2-374d2ca4adf1 · outbound

This paper cites A., Cubuk, E.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation A., Cubuk, E

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.643574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9765fe44-cc45-4390-a169-e822cecbf6c3 · outbound

This paper cites Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.467100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f1db6db-0d74-4802-b2ae-95a3a45b7165 · outbound

This paper cites Adversarial graph augmentation to improve graph contrastive learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Adversarial graph augmentation to improve graph contrastive learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.293898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7fbf65be-5b1f-4fc7-8579-a60fe2f5b103 · outbound

This paper cites Multi-view teacher with curriculum data fusion for robust unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Multi-view teacher with curriculum data fusion for robust unsupervised domain adaptation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:07.166066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 98a7fb90-6e99-4da9-89d1-79ccfe443d32 · outbound

This paper cites and Valpola, H.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Valpola, H

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:06.994461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 491488c5-46c5-4d50-976a-bfd6bb539bb1 · outbound

This paper cites and Radinsky, K.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Radinsky, K

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:06.679974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 01d06a17-bfce-4472-b177-2cc5bf460d86 · outbound

This paper cites Graph attention networks.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Graph attention networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:06.414199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 676c3a1d-f872-485f-b79d-b8f9eee1e6aa · outbound

This paper cites L., Li \`o , P., Bengio, Y., and Hjelm, R.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation L., Li \`o , P., Bengio, Y., and Hjelm, R

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:06.120697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb4d8369-5d2b-4be8-ad07-e4487f99c3f9 · outbound

This paper cites and Karypis, G.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Karypis, G

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:05.823802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 91efb7cd-dde9-416a-b39e-78ffc71ee871 · outbound

This paper cites Idea: An invariant perspective for efficient domain adaptive image retrieval.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Idea: An invariant perspective for efficient domain adaptive image retrieval

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:05.475423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5cf16e6b-efbd-4ebe-8446-2e9ca8dca94d · outbound

This paper cites A comprehensive graph pooling benchmark: Effectiveness, robustness and generalizability.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation A comprehensive graph pooling benchmark: Effectiveness, robustness and generalizability

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:41:58.487290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 271510a0-d7af-4da5-b4ff-58df623e68ac · outbound

This paper cites Metaalign: Coordinating domain alignment and classification for unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Metaalign: Coordinating domain alignment and classification for unsupervised domain adaptation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:05.208079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a43f4e7b-6cd3-4dcb-9759-7c20872ccfa0 · outbound

This paper cites Toalign: Task-oriented alignment for unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Toalign: Task-oriented alignment for unsupervised domain adaptation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:05.004264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8c14cd31-139e-4c88-9a15-d12642b048ff · outbound

This paper cites and Kipf, T.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Kipf, T

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:04.904122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea43fdc5-d0f4-4b79-bf34-0009c4a2f425 · outbound

This paper cites Unsupervised domain adaptive graph convolutional networks.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Unsupervised domain adaptive graph convolutional networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:04.628447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb4e10f5-841f-457b-bcf5-3839b80faa4a · outbound

This paper cites and Zhang, L.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation and Zhang, L

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:04.361904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f15b703-c5e8-408d-ab26-6a7c398cff5e · outbound

This paper cites Infogcl: Information-aware graph contrastive learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Infogcl: Information-aware graph contrastive learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:04.174280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3d6843d5-eae9-4b7f-8f1d-528d21d21cc0 · outbound

This paper cites How powerful are graph neural networks? In Proc.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation How powerful are graph neural networks? In Proc

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.939121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3211421e-124c-41cd-9d19-296b446d3d12 · outbound

This paper cites Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.662788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14ec7760-87a7-447b-8867-510b49b77846 · outbound

This paper cites Individual and structural graph information bottlenecks for out-of-distribution generalization.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Individual and structural graph information bottlenecks for out-of-distribution generalization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.526522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 791853c9-598b-4a1f-b8f7-8f3755a00a21 · outbound

This paper cites Deal: An unsupervised domain adaptive framework for graph-level classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Deal: An unsupervised domain adaptive framework for graph-level classification

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.422194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9865c293-779b-43ba-ad81-e31788fd2f41 · outbound

This paper cites CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:00.608377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c984f6e-014d-40c0-81dc-8498e5339f1b · outbound

This paper cites Hierarchical graph representation learning with differentiable pooling.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Hierarchical graph representation learning with differentiable pooling

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.248165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d300366d-6fb1-4d82-aae0-3220aab00c65 · outbound

This paper cites Graph domain adaptation via theory-grounded spectral regularization.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Graph domain adaptation via theory-grounded spectral regularization

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:03.084777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c64921a-a1a4-4d80-bca3-3db1ec85b6b0 · outbound

This paper cites Stable learning via sparse variable independence.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Stable learning via sparse variable independence

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:02.901169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb2758fd-fedd-4806-bee0-5b90fac096ab · outbound

This paper cites To-ugda: target-oriented unsupervised graph domain adaptation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation To-ugda: target-oriented unsupervised graph domain adaptation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:02.750344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b559c9bb-638d-4907-bb61-f4871cc92fb9 · outbound

This paper cites An end-to-end deep learning architecture for graph classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation An end-to-end deep learning architecture for graph classification

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:02.607556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a303e41d-5f4a-4869-8b52-6e6a0253a79d · outbound

This paper cites Adversarial separation network for cross-network node classification.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Adversarial separation network for cross-network node classification

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:02.354296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 887387ce-174f-48bf-91ed-3461dba5311f · outbound

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

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Cglb: Benchmark tasks for continual graph learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:02.161791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dcabd8dc-ca00-448c-bcb3-40f0b0b249f8 · outbound

This paper cites Adaptive disentangled transformer for sequential recommendation.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Adaptive disentangled transformer for sequential recommendation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:01.952502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1be257af-8da3-4f7b-b5c6-a4911e519c2b · outbound

This paper cites Generative causal interpretation model for spatio-temporal representation learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Generative causal interpretation model for spatio-temporal representation learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:01.739634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c21b7ae0-272c-4984-9219-7f3851d27681 · outbound

This paper cites Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:01.581069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66474a37-b7ba-4e07-8159-5fb1413790de · outbound

This paper cites Unsupervised domain adaptation for semantic segmentation via class-balanced self-training.

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation Unsupervised domain adaptation for semantic segmentation via class-balanced self-training

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:01.382519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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