Community detection robustness of graph neural networks | Paper Citation Record

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Community detection robustness of graph neural networks Unresolved cited work

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This paper cites We vary the mixing parameter µ ranging between 0.1 to 0.5 to investigate the effects of perturbations at different initial community strengths.

Community detection robustness of graph neural networks We vary the mixing parameter µ ranging between 0.1 to 0.5 to investigate the effects of perturbations at different initial community strengths

Reference 2

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This paper cites In the SBM, a graph with n vertices is partitioned into K different communities.

Community detection robustness of graph neural networks In the SBM, a graph with n vertices is partitioned into K different communities

Reference 3

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Community detection robustness of graph neural networks Unresolved cited work

Reference 4

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This paper cites We follow the procedure outlined by [31].

Community detection robustness of graph neural networks We follow the procedure outlined by [31]

Reference 5

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Community detection robustness of graph neural networks Unresolved cited work

Reference 6

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This paper cites They allow the learning of node representations based on both node features and graph structure.

Community detection robustness of graph neural networks They allow the learning of node representations based on both node features and graph structure

Reference 7

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Community detection robustness of graph neural networks Unresolved cited work

Reference 8

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This paper cites The key innovation is the sampling of a fixed-size neighborhood and the combination of the node’s own features with the aggregated neighborhood features.

Community detection robustness of graph neural networks The key innovation is the sampling of a fixed-size neighborhood and the combination of the node’s own features with the aggregated neighborhood features

Reference 9

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This paper cites The key idea is to formulate a continuous relaxation of the normalized MinCut problem and train- ing a GNN to compute optimal cluster assignments.

Community detection robustness of graph neural networks The key idea is to formulate a continuous relaxation of the normalized MinCut problem and train- ing a GNN to compute optimal cluster assignments

Reference 10

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This paper cites Given a graph with adjacency matrix A ∈ [0, 1]n×n and node features X ∈ Rn×p.

Community detection robustness of graph neural networks Given a graph with adjacency matrix A ∈ [0, 1]n×n and node features X ∈ Rn×p

Reference 11

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Community detection robustness of graph neural networks Unresolved cited work

Reference 12

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Community detection robustness of graph neural networks Unresolved cited work

Reference 13

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This paper cites ∆ECSpert = 1 |P| X p∈P |ECSinitial(p) − ECSfinal(p)| ECSinitial(p) ×100%, where P is the set of all perturbation types (mean, scale, random edge, targeted edge).

Community detection robustness of graph neural networks ∆ECSpert = 1 |P| X p∈P |ECSinitial(p) − ECSfinal(p)| ECSinitial(p) ×100%, where P is the set of all perturbation types (mean, scale, random edge, targeted edge)

Reference 14

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Community detection robustness of graph neural networks Unresolved cited work

Reference 15

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This paper cites Fortunato, Community detection in graphs, Phys.

Community detection robustness of graph neural networks Fortunato, Community detection in graphs, Phys

Reference 16

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Community detection robustness of graph neural networks Unresolved cited work

Reference 17

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This paper cites Girvan and M.

Community detection robustness of graph neural networks Girvan and M

Reference 18

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This paper cites Wandelt, X.

Community detection robustness of graph neural networks Wandelt, X

Reference 19

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This paper cites Lancichinetti and S.

Community detection robustness of graph neural networks Lancichinetti and S

Reference 20

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This paper cites Moriano, J.

Community detection robustness of graph neural networks Moriano, J

Reference 21

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This paper cites Aref and M.

Community detection robustness of graph neural networks Aref and M

Reference 22

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This paper cites Schuurman and E.

Community detection robustness of graph neural networks Schuurman and E

Reference 23

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Community detection robustness of graph neural networks Unresolved cited work

Reference 24

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This paper cites Scarselli, M.

Community detection robustness of graph neural networks Scarselli, M

Reference 25

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Community detection robustness of graph neural networks Unresolved cited work

Reference 26

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Observation 0639e70b-c18d-4eac-ab46-4a613f937a1c · outbound

This paper cites How Powerful are Graph Neural Networks?.

Community detection robustness of graph neural networks How Powerful are Graph Neural Networks?

Reference 27

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This paper cites Veliˇ ckovi´ c, Everything is connected: Graph neural net- works, Curr.

Community detection robustness of graph neural networks Veliˇ ckovi´ c, Everything is connected: Graph neural net- works, Curr

Reference 28

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

Community detection robustness of graph neural networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 29

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This paper cites Graph Attention Networks.

Community detection robustness of graph neural networks Graph Attention Networks

Reference 30

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This paper cites Hamilton, Z.

Community detection robustness of graph neural networks Hamilton, Z

Reference 31

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Community detection robustness of graph neural networks Unresolved cited work

Reference 32

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Community detection robustness of graph neural networks Unresolved cited work

Reference 33

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This paper cites A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource.

Community detection robustness of graph neural networks A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource

Reference 34

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Community detection robustness of graph neural networks Unresolved cited work

Reference 35

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Community detection robustness of graph neural networks Unresolved cited work

Reference 36

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This paper cites Tsitsulin, J.

Community detection robustness of graph neural networks Tsitsulin, J

Reference 37

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This paper cites Z¨ ugner, A.

Community detection robustness of graph neural networks Z¨ ugner, A

Reference 38

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This paper cites Adversarial Attacks on Graph Neural Networks via Meta Learning.

Community detection robustness of graph neural networks Adversarial Attacks on Graph Neural Networks via Meta Learning

Reference 39

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Community detection robustness of graph neural networks Unresolved cited work

Reference 40

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This paper cites G¨ unnemann, Graph neural networks: Adversarial ro- bustness, in Graph neural networks: foundations, fron- tiers, and applications (Springer, 2022) pp.

Community detection robustness of graph neural networks G¨ unnemann, Graph neural networks: Adversarial ro- bustness, in Graph neural networks: foundations, fron- tiers, and applications (Springer, 2022) pp

Reference 41

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This paper cites Zhang, X.

Community detection robustness of graph neural networks Zhang, X

Reference 42

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This paper cites Zhang, B.

Community detection robustness of graph neural networks Zhang, B

Reference 43

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Community detection robustness of graph neural networks Unresolved cited work

Reference 44

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This paper cites Tian and P.

Community detection robustness of graph neural networks Tian and P

Reference 45

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This paper cites Robustness of graph embedding methods for community detection.

Community detection robustness of graph neural networks Robustness of graph embedding methods for community detection

Reference 46

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This paper cites Lancichinetti, S.

Community detection robustness of graph neural networks Lancichinetti, S

Reference 47

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This paper cites Karrer and M.

Community detection robustness of graph neural networks Karrer and M

Reference 48

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Community detection robustness of graph neural networks Unresolved cited work

Reference 49

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Community detection robustness of graph neural networks Unresolved cited work

Reference 50

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This paper cites Synthetic Graph Generation to Benchmark Graph Learning.

Community detection robustness of graph neural networks Synthetic Graph Generation to Benchmark Graph Learning

Reference 51

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Community detection robustness of graph neural networks Unresolved cited work

Reference 52

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This paper cites Goel, Robust graph neural networks, https://github.com/jdg-stat/robustgnn (2025), gitHub repository.

Community detection robustness of graph neural networks Goel, Robust graph neural networks, https://github.com/jdg-stat/robustgnn (2025), gitHub repository

Reference 53

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This paper cites Lancichinetti and S.

Community detection robustness of graph neural networks Lancichinetti and S

Reference 54

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This paper cites Hagberg, P.

Community detection robustness of graph neural networks Hagberg, P

Reference 55

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Community detection robustness of graph neural networks Unresolved cited work

Reference 56

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Community detection robustness of graph neural networks Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-18T12:51:24.122072Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e2e40e4-b11c-485c-bfea-9d73b236e6ff · outbound

This paper cites Attacks on Node Attributes in Graph Neural Networks.

Community detection robustness of graph neural networks Attacks on Node Attributes in Graph Neural Networks

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:51:23.320349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4734b0b7-72b8-4a08-8688-97d102ac9d4f · outbound

This paper cites Souravlas, S.

Community detection robustness of graph neural networks Souravlas, S

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.116529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aef3febc-a638-4f95-9420-a854061024fd · outbound

This paper cites Sobolevsky and A.

Community detection robustness of graph neural networks Sobolevsky and A

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.228229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b3b22b0f-5dae-4faf-9c0d-fe6766911455 · outbound

This paper cites Leeney and R.

Community detection robustness of graph neural networks Leeney and R

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.253159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d348aa8-4454-426c-b96d-b8d5a74cabc9 · outbound

This paper cites an unresolved cited work.

Community detection robustness of graph neural networks Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-05-18T12:51:24.119224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a8ee9f39-3e41-411a-9f44-5620fb80d86c · outbound

This paper cites Merkwirth and T.

Community detection robustness of graph neural networks Merkwirth and T

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.130924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e7a2da63-4a2c-4281-8fc3-6ac36b706b56 · outbound

This paper cites an unresolved cited work.

Community detection robustness of graph neural networks Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-05-18T12:51:24.192305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 25d472c2-001f-4673-970f-e4ed9d89c713 · outbound

This paper cites Budel, M.

Community detection robustness of graph neural networks Budel, M

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.200931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f768043-aa6d-45cf-87eb-ed85edf2570f · outbound

This paper cites D´ esy, P.

Community detection robustness of graph neural networks D´ esy, P

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T12:51:24.212093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b9db52e-e12f-45d6-9c8a-ec8c93e3dd82 · outbound

This paper cites Hyperbolic Graph Neural Networks: A Review of Methods and Applications.

Community detection robustness of graph neural networks Hyperbolic Graph Neural Networks: A Review of Methods and Applications

Reference 67

Resolution: malformed identifier; arxiv_id, observed 2026-05-18T12:51:23.344792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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