Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios | Paper Citation Record

Observation 5d310243-7f65-4f77-9fd2-d212392e540d · outbound

This paper cites write newline.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:39.540588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e39d4378-3ec2-471e-8827-bfcbea7b6d6a · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios A simple framework for contrastive learning of visual representations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:39.626536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 840bacfb-c5d2-40d0-b0fb-4bb7583aab2f · outbound

This paper cites an unresolved cited work.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:39.704049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53366178-a8e1-4dec-bb2b-b977bb57619f · outbound

This paper cites Efficient and adaptive recommendation unlearning: A guided filtering framework to erase outdated preferences.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Efficient and adaptive recommendation unlearning: A guided filtering framework to erase outdated preferences

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:59.136523Z

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 a68b63ae-8281-4337-9127-ac15a9955cc5 · outbound

This paper cites Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:39.900616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84c708fa-fbf5-4d39-89ca-8cba3dcad2b6 · outbound

This paper cites Iterative deep structural graph contrast clustering for multiview raw data.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Iterative deep structural graph contrast clustering for multiview raw data

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T13:40:47.990699Z

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 dd9adcfe-7645-44b3-9557-36cecfb6e49f · outbound

This paper cites Cross-view topology based consistent and complementary information for deep multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-view topology based consistent and complementary information for deep multi-view clustering

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:58.869551Z

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 c3a98c81-5d33-4fa3-acb1-d3a45ff3a30a · outbound

This paper cites Robust contrastive multi-view clustering against dual noisy correspondence.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust contrastive multi-view clustering against dual noisy correspondence

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:58.622804Z

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 096b5d41-4447-4a69-828f-871381820b6d · outbound

This paper cites and Khasahmadi, A.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Khasahmadi, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:58.326589Z

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 884693e3-5c2f-4cd2-9fbb-a7661d05ab1b · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Momentum contrast for unsupervised visual representation learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:58.098963Z

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 f5616db8-40ad-417d-86d6-439c5469a4ae · outbound

This paper cites an unresolved cited work.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:40:57.827686Z

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 01dc1802-1a61-4def-ab2d-eec9c57bc34c · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Learning deep representations by mutual information estimation and maximization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:40.525038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd089569-aa36-4ff1-aa77-6e6458cea080 · outbound

This paper cites Exploring the role of node diversity in directed graph representation learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Exploring the role of node diversity in directed graph representation learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:57.615499Z

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 16d6e22e-ff9b-40dd-9d6f-c19717c3d73c · outbound

This paper cites On which nodes does gcn fail? enhancing gcn from the node perspective.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios On which nodes does gcn fail? enhancing gcn from the node perspective

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:57.406113Z

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 10cd35d1-90de-4905-b74b-87e23343c94b · outbound

This paper cites T., Lv, J., and Peng, X.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios T., Lv, J., and Peng, X

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:57.158021Z

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 6e3bbbaf-cd9b-4742-bdaf-529d0294498c · outbound

This paper cites and Dayan, P.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Dayan, P

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:40.871593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d7b9281-c906-4f4d-bd73-740a643e2789 · outbound

This paper cites B., and Kanagachidambaresan, G.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios B., and Kanagachidambaresan, G

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:56.929437Z

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 1ca27260-e5ee-4540-9329-8417597bb187 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Adam: A Method for Stochastic Optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:41.079331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cfda89f-c208-4c43-b52e-a4272f837ca8 · outbound

This paper cites Cross-view graph matching guided anchor alignment for incomplete multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-view graph matching guided anchor alignment for incomplete multi-view clustering

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:56.678367Z

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 5f6f07b8-939b-4ed1-87b7-26a0d13a47c9 · outbound

This paper cites P., Sun, Y., Sun, Q., Sun, Y., W.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios P., Sun, Y., Sun, Q., Sun, Y., W

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:56.466352Z

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 e8471845-5264-43fa-85b2-857f2faa26d0 · outbound

This paper cites Consensus graph learning for multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Consensus graph learning for multi-view clustering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:56.234976Z

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 37d85401-77e6-4c7a-a28e-43eb94b1885b · outbound

This paper cites Efficient one-pass multi-view subspace clustering with consensus anchors.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Efficient one-pass multi-view subspace clustering with consensus anchors

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:56.023150Z

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 9db22d80-9009-449c-a273-9876fdc5eb91 · outbound

This paper cites One pass late fusion multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios One pass late fusion multi-view clustering

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:55.821074Z

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 2c49fc6a-8fa3-4d5b-b20d-1cfd3ee9819f · outbound

This paper cites Deep graph clustering via dual correlation reduction.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Deep graph clustering via dual correlation reduction

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:55.581291Z

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 5eb40e4d-3ecc-43a9-9319-de8e9e0be58e · outbound

This paper cites Simple contrastive graph clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Simple contrastive graph clustering

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:55.339344Z

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 576dd715-26ba-4d61-ad98-cd25559f3f43 · outbound

This paper cites Decoupled contrastive multi-view clustering with high-order random walks.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Decoupled contrastive multi-view clustering with high-order random walks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:55.160543Z

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 379bdbb1-f656-4029-a9c1-2251a28d5335 · outbound

This paper cites Revisiting self-supervised heterogeneous graph learning from spectral clustering perspective.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Revisiting self-supervised heterogeneous graph learning from spectral clustering perspective

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:54.895835Z

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 dd0494cf-7855-4ee1-8f50-73585fc2c247 · outbound

This paper cites Hg-adapter: Improving pre-trained heterogeneous graph neural networks with dual adapters.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Hg-adapter: Improving pre-trained heterogeneous graph neural networks with dual adapters

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:54.651877Z

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 917398da-4e9a-4439-8047-c2356531e8a2 · outbound

This paper cites Robust multi-view clustering with noisy correspondence.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust multi-view clustering with noisy correspondence

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:54.405459Z

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 68b2896f-7475-49aa-991c-fea78bbe02fd · outbound

This paper cites Contrastive multiview coding.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Contrastive multiview coding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:42.393575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f50e36b-6251-4df0-99e8-81b8d7d8bffc · outbound

This paper cites J., Lokse, S., Jenssen, R., and Kampffmeyer, M.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios J., Lokse, S., Jenssen, R., and Kampffmeyer, M

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:54.177620Z

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 6558fe7f-0ea3-4394-8d22-ac695224f2c4 · outbound

This paper cites Self-supervised Learning from a Multi-view Perspective.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Self-supervised Learning from a Multi-view Perspective

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:42.660184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d624b8-bfcb-4538-8604-146073a241b9 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Representation Learning with Contrastive Predictive Coding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:42.798243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bf48704-bab2-4f8b-943d-867c019f079f · outbound

This paper cites and Hinton, G.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios and Hinton, G

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:42.937336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00b10cbe-1ae8-4423-bbcc-12b87793136d · outbound

This paper cites Continual multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Continual multi-view clustering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:53.931119Z

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 1ca7f22e-6b19-47dd-9629-1cc1750d4bf8 · outbound

This paper cites Fast continual multi-view clustering with incomplete views.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Fast continual multi-view clustering with incomplete views

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:43.152457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb9695ff-84b4-433d-814d-92f80f963db8 · outbound

This paper cites View gap matters: Cross-view topology and information decoupling for multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios View gap matters: Cross-view topology and information decoupling for multi-view clustering

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:53.658737Z

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 a595325a-2973-42e6-983b-4add3a98cae3 · outbound

This paper cites Evaluate then cooperate: Shapley-based view cooperation enhancement for multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Evaluate then cooperate: Shapley-based view cooperation enhancement for multi-view clustering

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:53.411357Z

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 d321cf65-8797-4752-9c9a-6e7e7594ce45 · outbound

This paper cites Generative partial multi-view clustering with adaptive fusion and cycle consistency.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Generative partial multi-view clustering with adaptive fusion and cycle consistency

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:53.150768Z

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 5ada7131-693b-48bf-8d3c-ff9c39540e2b · outbound

This paper cites Incomplete multi-view clustering via graph regularized matrix factorization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Incomplete multi-view clustering via graph regularized matrix factorization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.884729Z

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 dbee3f3c-6896-4dda-a783-16ffc7b38bed · outbound

This paper cites Y., and He, L.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Y., and He, L

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.689401Z

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 a21512c3-69e5-4ccd-a191-0a54880ba175 · outbound

This paper cites Multi-level feature learning for contrastive multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Multi-level feature learning for contrastive multi-view clustering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.592879Z

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 422ffd17-3de4-4ce3-9e0d-816456f03c66 · outbound

This paper cites Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Investigating and mitigating the side effects of noisy views for self-supervised clustering algorithms in practical multi-view scenarios

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.439950Z

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 7dd465a0-c012-4e08-a6e3-937161cfbf26 · outbound

This paper cites Partially view-aligned representation learning with noise-robust contrastive loss.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Partially view-aligned representation learning with noise-robust contrastive loss

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.288683Z

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 fd336e6b-fa11-4ca6-b552-2b800aa3921d · outbound

This paper cites Robust multi-view clustering with incomplete information.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Robust multi-view clustering with incomplete information

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:52.134913Z

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 d80265ea-6f40-4213-9c14-f367b31a33f5 · outbound

This paper cites Interpolation-based contrastive learning for few-label semi-supervised learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Interpolation-based contrastive learning for few-label semi-supervised learning

Reference 46

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T13:40:47.595133Z

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 da45e0d1-4523-40c9-b3f3-549c1dc21d61 · outbound

This paper cites Dealmvc: Dual contrastive calibration for multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dealmvc: Dual contrastive calibration for multi-view clustering

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:51.987075Z

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 bf4a7a42-ed14-488c-91f3-9bccbad18f1f · outbound

This paper cites Cluster-guided contrastive graph clustering network.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cluster-guided contrastive graph clustering network

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:51.806400Z

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 1a114e0e-5d7f-4c95-a4cb-98f8e23010eb · outbound

This paper cites Z., Liu, X., and Zhu, E.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Z., Liu, X., and Zhu, E

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:51.502506Z

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 6cd36560-239d-449b-b950-2a06d4ca114f · outbound

This paper cites Hyperbolic contrastive learning for cross-domain recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Hyperbolic contrastive learning for cross-domain recommendation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:51.214556Z

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 768c67f0-b33f-47c5-bc69-546db11928cf · outbound

This paper cites Graphlearner: Graph node clustering with fully learnable augmentation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Graphlearner: Graph node clustering with fully learnable augmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.962305Z

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 7e46bf3d-419a-45ed-ae09-1a48f88e8758 · outbound

This paper cites Mixed graph contrastive network for semi-supervised node classification.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Mixed graph contrastive network for semi-supervised node classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.689681Z

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 aaf40fec-ae85-48ff-b732-b2d3d90361d1 · outbound

This paper cites Darec: A disentangled alignment framework for large language model and recommender system.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Darec: A disentangled alignment framework for large language model and recommender system

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.436593Z

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 8540a564-519f-445a-9340-28cf9af29903 · outbound

This paper cites Dual test-time training for out-of-distribution recommender system.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dual test-time training for out-of-distribution recommender system

Reference 54

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T13:40:46.168958Z

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 a3056988-ee64-48b7-b3da-c4fb468b3500 · outbound

This paper cites Apgl4sr: A generic framework with adaptive and personalized global collaborative information in sequential recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Apgl4sr: A generic framework with adaptive and personalized global collaborative information in sequential recommendation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.216236Z

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 90418e67-1c52-47d0-b460-79e1d4b6e0c2 · outbound

This paper cites Dataset regeneration for sequential recommendation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dataset regeneration for sequential recommendation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:50.032797Z

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 66dbc22f-fbc3-4159-aa09-cb5d5031adfd · outbound

This paper cites Gzoo: Black-box node injection attack on graph neural networks via zeroth-order optimization.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Gzoo: Black-box node injection attack on graph neural networks via zeroth-order optimization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.791620Z

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 3c9e1a3e-4a36-406c-8705-a93c3793226f · outbound

This paper cites Dshield: Defending against backdoor attacks on graph neural networks via discrepancy learning.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Dshield: Defending against backdoor attacks on graph neural networks via discrepancy learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.524095Z

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 6fe5e819-1e39-417e-ba4a-e0bbdc7aeaf0 · outbound

This paper cites Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Sparse low-rank multi-view subspace clustering with consensus anchors and unified bipartite graph

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.313831Z

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 3e70332c-dfba-4a8b-9c12-130168ff4186 · outbound

This paper cites How to construct corresponding anchors for incomplete multiview clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios How to construct corresponding anchors for incomplete multiview clustering

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:49.112508Z

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 c3475c05-d7b2-49bc-a48b-59a1a839b187 · outbound

This paper cites Towards resource-friendly, extensible and stable incomplete multi-view clustering.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Towards resource-friendly, extensible and stable incomplete multi-view clustering

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.889333Z

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 3ac7ed0a-79f1-4599-818c-586e06908fdc · outbound

This paper cites Cross-domain recommendation via user interest alignment.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-domain recommendation via user interest alignment

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.682733Z

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 6e757ca9-4878-489f-a7d4-e89ef2298e7b · outbound

This paper cites Cross-domain recommendation via progressive structural alignment.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Cross-domain recommendation via progressive structural alignment

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.506732Z

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 0a77508c-9a42-494a-821e-b6182b5b8281 · outbound

This paper cites Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.371152Z

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 4735574e-23c1-4dc4-9f26-98c4463895f6 · outbound

This paper cites Multiple kernel clustering with neighbor-kernel subspace segmentation.

Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios Multiple kernel clustering with neighbor-kernel subspace segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:40:48.197475Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers