Pith. sign in

REVIEW 4 major objections 5 minor 102 references

FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read FedHERO claims that sharing a learned latent-graph generator across clients makes federated node classification work on heterophilic graphs, while a private local channel preserves client-specific topology.

desk verdict Real problem, plausible dual-channel design, but the hard top-k latent graph makes the shared structure learner untrainable as written; the central mechanism needs a differentiable fix. read the letter →

arxiv 2504.21206 v2 pith:ZCWMM2MO submitted 2025-04-29 cs.LG cs.AIcs.DC

Zihan Chen , Xingbo Fu , Yushun Dong , Jundong Li , Cong Shen This is my paper · ORCID
classification cs.LGcs.AIcs.DC
keywords federatedgraphlearningheterophilicgraphsnodeclassificationstructuredual-channelGNNaggregationprivacy-preserving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Federated graph learning usually assumes that all clients' graphs are homophilic, so that the GNNs trained on them capture compatible patterns and can be safely averaged. This paper argues that the assumption fails in practice, because clients may hold graphs with different heterophily levels, and standard aggregation such as FedAvg makes the global model worse than local training. To fix it, the paper proposes FedHERO, which splits each client model into a shared global channel driven by a learned latent graph and a private local channel that operates on the original graph, and claims this design consistently outperforms existing federated graph learning baselines on node classification. If that claim holds, federated node classification no longer needs to assume homophily to benefit from collaboration.

What carries the argument

The load-bearing object is the dual-channel GNN with a shared structure learner. The structure learner is a one-layer GNN plus a multi-head weighted-attention metric $\phi(z_u,z_v) = \frac{1}{N_H}\sum_h \cos(w_h^1 \odot z_u, w_h^2 \odot z_v)$ that scores each candidate edge, and a top-$k$ selection turns the scores into a latent graph $\tilde{A}$. The global channel propagates on $\tilde{A}$, the local channel propagates on the original $A$, and the layer output is a convex combination $Z^\ell = \sigma(\alpha E^\ell + (1-\alpha) H^\ell)$. Only the global-channel parameters $w_g$ are aggregated as $w_g = \sum_i \frac{N_i}{N} w_i^g$, which is what lets clients share a common message-passing pattern while keeping their own topology private.

What would settle it

Measure the class-mixing pattern of the latent graphs each client produces after federation, for example by computing the Jensen-Shannon divergence between neighbor-class distributions of latent graphs across client pairs; if those divergences are not smaller than the same divergences computed on the original local graphs, the shared structure learner is not aligning the clients and the reported gains must come from some other component. Alternatively, train FedHERO with the shared structure learner but randomize the server-side averaging of global channels and check whether the accuracy gap to FedAvg persists.

Watch

Extended reading notes

Core claim

The central claim is that the obstacle to federated learning on heterophilic graphs is not heterophily itself but the mismatch between neighbor-distribution patterns across clients, and that this mismatch can be removed by learning structure. FedHERO gives each client a structure learner that scores edges between nodes with a multi-head weighted attention metric, applies a top-k sparsification to build a latent graph, and then runs a global-channel GNN on that latent graph while a local-channel GNN runs on the original adjacency. Only the global channel parameters are sent to the server and averaged by node-count weighting; the local channel and classifier stay private. The authors assert that the shared structure learner makes the latent graphs from different clients exhibit similar neighbor-distribution patterns, so the aggregated global-channel GNNs capture consistent knowledge, and that FedHERO consistently outperforms all tested federated graph learning baselines on four semi-synthetic heterophilic datasets and three real-world datasets.

Load-bearing premise

Everything rests on the premise that averaging locally trained structure learners makes the latent graphs they generate similar enough across clients that aggregating the global-channel models helps; if the latent graphs stay as mismatched as the original graphs, the whole benefit disappears.

Editorial extensions

If this is right

  • Federated node classification no longer has to assume client graphs are homophilic; clients with different heterophily levels can still get a benefit from aggregation.
  • Only the global channel is shared, so client-specific topology and the classifier stay private; the paper's link-inference attack results show that sharing this smaller part leaks less structural information than sharing whole models.
  • Because message passing runs on a learned latent graph rather than the raw adjacency, flipping ten percent of edges degrades FedHERO less than it degrades the baselines.
  • The structure learner is a swappable module: attention, GAT, cosine distance, GLCN, and GAug variants all work, with the multi-head attention version performing best in the paper's comparisons.
  • The performance gap over the best baseline grows on datasets with lower homophily ratios, indicating that the method targets exactly the regime where existing federated graph learning methods fail.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same dual-channel recipe could transplant to other federated settings where clients share a task but disagree about the local geometry of the input space: learn a shared canonical neighborhood structure, aggregate only the models trained on it, and keep the local geometry private.
  • The paper's explanation implies a testable monotonicity: across a synthetic sweep of cross-client neighbor-distribution mismatch, FedHERO's advantage over FedAvg should widen as the mismatch grows; such a sweep would isolate the mechanism from the architecture's other benefits.
  • The privacy claim is bounded by the attack tested: sharing a structure learner that maps features to edge scores may still leak topology through the shared parameters, so the reported privacy gain is relative to link inference from node representations rather than a formal guarantee.
  • The top-k post-processing may be doing more than sparsifying: the Bernoulli-sampling variant performs worse and less stably, suggesting that the fixed, discrete degree of the latent graph, not just the learned edge scores, contributes to cross-client alignment.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes FedHERO, a federated graph learning framework for node classification on heterophilic graphs. Each client maintains a dual-channel GNN: a global channel that operates on a latent graph generated by a structure learner shared across clients, and a private local channel that operates on the original graph. The server aggregates only the global-channel models via FedAvg-style averaging. The paper claims that this design enables clients to share structural knowledge without aggregating conflicting neighbor-distribution patterns, and reports consistent improvements over FGL baselines on semi-synthetic and real-world datasets. The appendices include additional experiments on overlapping subgraphs, latent-graph generation variants, hyperparameter sensitivity, and a detailed comparison with FedStar.

Significance. If the technical concern about differentiability is resolved, the paper makes a useful contribution: a dual-channel architecture with a shared structure learner is a plausible way to reduce cross-client inconsistency in federated learning on heterophilic graphs. The evaluation is broad: four semi-synthetic and three real-world datasets, two partitioning methods, multiple client counts, ablation studies on structure-learning variants and sharing mechanisms, a robustness study, a privacy analysis, and a convergence comparison. The paper also clearly identifies a real problem in FGL. However, because the global channel's claimed training mechanism is not actually optimized as written, these strengths cannot be credited until the method is corrected.

major comments (4)
  1. [Section 3.1, Eq. (4), and Appendix C.2] The hard top-k selection in Eq. (4) is not differentiable as claimed. The entries of the binary matrix \tilde A are piecewise-constant functions of the continuous scores from Eq. (3), so the derivatives of the cross-entropy loss in Eq. (8) and of the smoothness regularizer in Eq. (7) with respect to the structure-learner parameters theta vanish almost everywhere. As written, the structure learner g_L receives no gradient from the training loss, so it cannot be trained from data and the central mechanism of sharing a learned structure learner across clients is unsupported. Please replace Eq. (4) with a differentiable relaxation (for example, a soft top-k with temperature or a perturbed-optimizer estimator) or explicitly describe and analyze a straight-through estimator, and then verify empirically that the structure learner parameters actually change during training.
  2. [Section 4.3, after Figure 3] The text first states that FedHERO 'consistently outperforms all FGL baselines on ieee-fraud and credit datasets' and then immediately attributes 'similar performance' on the same two datasets to the ease of the classification task. These statements are contradictory. The authors should report the real-world results accurately and qualify the 'consistently outperforms' claim accordingly, both in Section 4.3 and in the abstract.
  3. [Section 3.4, Table 2] The reported computational cost for FedHERO, O(Ld(d|V|+|E|)+NHd|V|), does not account for constructing the kNN latent graph. Under Eq. (4), every node must be scored against every other node through the metric in Eq. (3), which requires O(NH |V|^2) pairwise computations per client. The complexity table therefore understates the true cost and the claim that FedHERO scales to large graphs 'without introducing much computation burden' is not justified. Please correct the analysis or state an explicit approximation (such as neighbor sampling) that makes the bound valid.
  4. [Section 4.2] The assertion that FedHERO 'consistently outperforms all the baseline methods by a significant margin' is stronger than the data in Table 3 supports. For several configurations (for instance, some of the Actor and Flickr columns), the margin over the best baseline is comparable to the reported standard deviation. The empirical claims should be quantified with effect sizes and uncertainty, not just average accuracy.
minor comments (5)
  1. [Section 4.2] The statement that 'FedAvg often underperforms compared to local training in many experiments' is contradicted by several entries in Table 3 (for example, Actor under METIS M=5, where FedAvg exceeds Local); please soften the claim to reflect the mixed pattern.
  2. [Appendix C.2] The same incorrect claim that the top-k function is differentiable appears verbatim in the appendix; any correction in Section 3.1 must be applied here as well.
  3. [Section 4.7] The link inference attack is cited to Gong and Liu (2018), but that paper describes attribute inference attacks; please provide the appropriate citation for the link inference setting or define the attack precisely.
  4. [Section 2, Eq. (2)] The objective writes g_L(X_i, A_i) without specifying that g_L includes both the one-layer GNN that produces node representations and the metric function of Eq. (3); please make this compositional structure explicit.
  5. [Figure 2 and Table 3] The notation is hard to follow: the terms f_local, f_global, fl_loc, fl_g, and fc are used interchangeably, and the column headers in Table 3 merge dataset names with M values. Please unify the notation and reformat the table.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: FedHERO's central claims are empirical, benchmarked against external baselines, with no fitted quantity relabeled as a prediction.

full rationale

The paper's central assertion is an empirical performance claim: FedHERO is compared against published, external baselines (FedAvg, FedPub, FedStar, GCFL, FedLit, FedSage) on held-out test partitions, so the outcome is not defined by the method's own fitted values. The structure learner, metric function, dual-channel GNN, and loss in Eq. (8) are standard model components; the claim that sharing the structure learner aligns latent graph distributions across clients (Section 4.2) is an asserted mechanism rather than a proven theorem, but an unsupported or weakly supported premise is a correctness and evidence concern, not circularity. Citations such as Chen et al. (2020) for the attention metric and FedStar-related work are background references, not author-overlapping results that define the predicted quantities or forbid alternatives. The hard top-k step in Eq. (4) and its asserted differentiability raise a legitimate correctness issue about whether gradients reach the structure learner, but that is a flaw in the stated training loop, not a definitional reduction of the output to the input. The inconsistent wording in Section 4.3 (claims of consistent outperformance alongside 'similar performance' and FedSage superiority on syn-cora) is an empirical reporting inconsistency, not circularity. Overall, no step in the paper reduces by construction to its own inputs, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 5 free parameters · 4 assumptions · 2 invented entities

The paper introduces no new physical entities; the latent graph and structure learner are model artifacts whose 'universality' is an unverified assumption. The method depends on several hand-set hyperparameters (alpha, lambda, mu, k, NH) and on the unproven premise that shared structure learners produce alignable latent graphs. The most consequential unstated assumption is the differentiability of the hard top-k operation, which is not standard and is essential to the claimed training signal for the structure learner.

free parameters (5)
  • alpha (local/global channel mixing weight) = 0.2
    Controls the blend of local-channel and global-channel embeddings in Eq. (5); fixed by hand across all main experiments, with 0.4 better for Flickr (Appendix D.3).
  • lambda (smoothness regularization weight) = 0.1
    Weight of the feature-smoothness term in Eq. (7); fixed by hand.
  • mu (Frobenius norm weight) = 0.1
    Weight of the degree-regularization term in Eq. (7); fixed by hand.
  • k (nearest neighbors in latent graph) = 20
    Number of edges kept per node in the kNN latent graph, Eq. (4); fixed across datasets, chosen via sensitivity study in Appendix C.2.
  • NH (number of attention heads) = 4
    Number of heads in the metric function, Eq. (3); fixed by hand.
assumptions (4)
  • domain assumption Shared structure learning produces latent graphs with similar neighbor distributions across clients
    Stated in Section 4.2: 'By sharing the structure learning model across clients, FedHERO ensures that the latent graphs generated by different clients exhibit similar neighbor distribution patterns.' This premise is essential for FedAvg aggregation of the global channel to help, but it is not proven.
  • ad hoc to paper The top-k function used to build the latent graph is differentiable
    Section 3.1 claims differentiability 'facilitates the computation of parameter gradients', but the hard 0/1 adjacency in Eq. (4) has zero gradient almost everywhere with respect to the attention scores unless a straight-through estimator is used, which is not described.
  • domain assumption Feature-smoothness regularization is useful on heterophilic graphs
    Eq. (7) encourages connections between nodes with similar features; this is in tension with heterophily, where labels differ across edges, and the paper only justifies it empirically.
  • domain assumption Sharing only the global channel reduces privacy leakage
    Section 4.7 claims privacy benefits from sharing fewer parameters, but a smaller shared parameter set is not a privacy guarantee; the LIA experiment measures link inference, not the general privacy claim.
invented entities (2)
  • Latent graph A~
    purpose: A generated adjacency used by the global-channel GNN to convey patterns that are assumed universal across clients
    No evidence outside the optimization objective that such cross-client universal patterns exist; the paper's own Appendix B shows local distributions differ sharply.
  • Universal structure learner g_L
    purpose: The shared model that produces latent graphs from local node features
    The 'universality' is an assertion; the model is fit on local data and averaged, with no external validation of its generality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs." pith.science (2026). https://pith.science/paper/ZCWMM2MO

@misc{pith2026250421206,
  author       = {Pith},
  title        = {Pith review of: FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZCWMM2MO}},
  note         = {Machine review of arXiv:2504.21206}
}
read the original abstract

Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods usually require that the graph data owned by all clients is homophilic to ensure similar neighbor distribution patterns of nodes. Such an assumption ensures that the learned knowledge is consistent across the local models from all clients. Therefore, these local models can be properly aggregated as a global model without undermining the overall performance. Nevertheless, when the neighbor distribution patterns of nodes vary across different clients (e.g., when clients hold graphs with different levels of heterophily), their local models may gain different and even conflict knowledge from their node-level predictive tasks. Consequently, aggregating these local models usually leads to catastrophic performance deterioration on the global model. To address this challenge, we propose FedHERO, an FGL framework designed to harness and share insights from heterophilic graphs effectively. At the heart of FedHERO is a dual-channel GNN equipped with a structure learner, engineered to discern the structural knowledge encoded in the local graphs. With this specialized component, FedHERO enables the local model for each client to identify and learn patterns that are universally applicable across graphs with different patterns of node neighbor distributions. FedHERO not only enhances the performance of individual client models by leveraging both local and shared structural insights but also sets a new precedent in this field to effectively handle graph data with various node neighbor distribution patterns. We conduct extensive experiments to validate the superior performance of FedHERO against existing alternatives.

Figures

Figures reproduced from arXiv: 2504.21206 by the authors.

Figure 1
Figure 1. An example of financial transaction net [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The framework of FedHERO. Blue boxes represent models in the flocal channel, which are trained locally and personalized for each client. Yellow boxes denote models in the fglobal channel, shared among clients to train a structure learner for mutual benefit. On the right side of the figure, the model aggregation scheme in FedHERO is depicted. function, following the approach outlined by Chen et al. (2020): ϕ(zu, zv) … view at source ↗
Figure 3
Figure 3. Performance of FedHERO and baselines on three real-world datasets [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: (Left) Link inference attack (LIA) accu￾racies on FedHERO and FGL baseline methods. (Right) Performance of FedHERO and FGL base￾line methods on noisy graph datasets. 4.7 Privacy Preservation in FedHERO FedHERO only transmits the components in the fglobal channel to the…
Figure 6
Figure 6. Figure 6: Neighbor node distributions of two clients in two datasets respectively. Each bar represents the [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]
Figure 7
Figure 7. Figure 7: Neighbor node distributions of clients in two datasets split by METIS. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: The sensitivity study of hyperparameter k. The star point in the figure corresponds to the default result reported in the main text. We also examine the influence of another preva￾lent latent graph generation method (Wu et al., 2020a; Elinas et al., 2020; Zhao et al., …
Figure 9
Figure 9. Figure 9: Performance of FedHERO with different hyperparameter settings. The star point in the figure corresponds to the default result reported in the main text. D Influence of Hyperparameters in FedHERO D.1 Hyperparameter Settings. The architecture of the GNN models consists o…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

102 extracted references · 46 canonical work pages

  1. [1]

    Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing

    Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning, pp.\ 21--29. PMLR, 2019

    2019

  2. [2]

    Personalized subgraph federated learning

    Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, and Sung Ju Hwang. Personalized subgraph federated learning. In International Conference on Machine Learning, pp.\ 1396--1415. PMLR, 2023

    2023

  3. [3]

    Data decisions and theoretical implications when adversarially learning fair representations

    Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075, 2017

    arXiv 2017

  4. [4]

    Node classification in social networks

    Smriti Bhagat, Graham Cormode, and S Muthukrishnan. Node classification in social networks. Social network data analytics, pp.\ 115--148, 2011

    2011

  5. [5]

    Fast unfolding of communities in large networks

    Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008 0 (10): 0 P10008, 2008

    2008

  6. [6]

    Beyond low-frequency information in graph convolutional networks

    Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. Beyond low-frequency information in graph convolutional networks. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pp.\ 3950--3957, 2021

    2021

  7. [7]

    Geometric deep learning: going beyond euclidean data

    Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34 0 (4): 0 18--42, 2017

    2017

  8. [8]

    Fedgl: Federated graph learning framework with global self-supervision

    Chuan Chen, Weibo Hu, Ziyue Xu, and Zibin Zheng. Fedgl: Federated graph learning framework with global self-supervision. arXiv preprint arXiv:2105.03170, 2021 a

    arXiv 2021

Show all 102 references
  1. [9]

    Fede: Embedding knowledge graphs in federated setting

    Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, and Huajun Chen. Fede: Embedding knowledge graphs in federated setting. In Proceedings of the 10th International Joint Conference on Knowledge Graphs, pp.\ 80--88, 2021 b

    2021

  2. [10]

    Iterative deep graph learning for graph neural networks: Better and robust node embeddings

    Yu Chen, Lingfei Wu, and Mohammed Zaki. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Advances in neural information processing systems, 33: 0 19314--19326, 2020

    2020

  3. [11]

    Adaptive universal generalized pagerank graph neural network

    Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988, 2020

    2006 arXiv

  4. [12]

    Grapheditor: An efficient graph representation learning and unlearning approach

    Weilin Cong and Mehrdad Mahdavi. Grapheditor: An efficient graph representation learning and unlearning approach. 2022

    2022

  5. [13]

    A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability

    Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability. arXiv preprint arXiv:2204.08570, 2022

    2022 arXiv

  6. [14]

    Benchmarking graph neural networks

    Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Benchmarking graph neural networks. Journal of Machine Learning Research, 24 0 (43): 0 1--48, 2023

    2023

  7. [15]

    Fairness through awareness

    Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pp.\ 214--226, 2012

    2012

  8. [16]

    Variational inference for graph convolutional networks in the absence of graph data and adversarial settings

    Pantelis Elinas, Edwin V Bonilla, and Louis Tiao. Variational inference for graph convolutional networks in the absence of graph data and adversarial settings. Advances in Neural Information Processing Systems, 33: 0 18648--18660, 2020

    2020

  9. [17]

    Graph neural networks for social recommendation

    Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph neural networks for social recommendation. In The world wide web conference, pp.\ 417--426, 2019

    2019

  10. [18]

    Learning discrete structures for graph neural networks

    Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. Learning discrete structures for graph neural networks. In International conference on machine learning, pp.\ 1972--1982. PMLR, 2019

    1972

  11. [19]

    Federated learning: A signal processing perspective

    Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C Eldar, and H Vincent Poor. Federated learning: A signal processing perspective. IEEE Signal Processing Magazine, 39 0 (3): 0 14--41, 2022

    2022

  12. [20]

    Diffusion improves graph learning

    Johannes Gasteiger, Stefan Wei enberger, and Stephan G \"u nnemann. Diffusion improves graph learning. Advances in neural information processing systems, 32, 2019

    2019

  13. [21]

    Attribute inference attacks in online social networks

    Neil Zhenqiang Gong and Bin Liu. Attribute inference attacks in online social networks. ACM Transactions on Privacy and Security (TOPS), 21 0 (1): 0 1--30, 2018

    2018

  14. [22]

    Spreadgnn: Serverless multi-task federated learning for graph neural networks

    Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, and Salman Avestimehr. Spreadgnn: Serverless multi-task federated learning for graph neural networks. arXiv preprint arXiv:2106.02743, 2021

    2021 arXiv

  15. [23]

    Ieee-cis fraud detection, 2019

    Addison Howard, Bernadette Bouchon-Meunier, IEEE CIS, inversion, John Lei, Lynn@Vesta, Marcus2010, and Hussein Abbass. Ieee-cis fraud detection, 2019. URL https://kaggle.com/competitions/ieee-fraud-detection

    2019

  16. [24]

    Semi-supervised learning with graph learning-convolutional networks

    Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo. Semi-supervised learning with graph learning-convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 11313--11320, 2019

    2019

  17. [25]

    Graph structure learning for robust graph neural networks

    Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp.\ 66--74, 2020

    2020

  18. [26]

    Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices

    George Karypis and Vipin Kumar. Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. 1997

    1997

  19. [27]

    Differentiable graph module (dgm) for graph convolutional networks

    Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael M Bronstein. Differentiable graph module (dgm) for graph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 0 (2): 0 1606--1617, 2022

    2022

  20. [28]

    How to find your friendly neighborhood: Graph attention design with self-supervision

    Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv preprint arXiv:2204.04879, 2022

    2022 arXiv

  21. [29]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  22. [30]

    Semi-supervised classification with graph convolutional networks

    Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016

    2016 arXiv

  23. [31]

    Rethinking graph transformers with spectral attention

    Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent L \'e tourneau, and Prudencio Tossou. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34: 0 21618--21629, 2021

    2021

  24. [32]

    Federated learning over coupled graphs

    Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, and Xiaohong Guan. Federated learning over coupled graphs. IEEE Transactions on Parallel and Distributed Systems, 34 0 (4): 0 1159--1172, 2023

    2023

  25. [33]

    Adaptive graph convolutional neural networks

    Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. Adaptive graph convolutional neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018 a

    2018

  26. [34]

    Adaptive graph convolutional neural networks

    Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. Adaptive graph convolutional neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018 b

    2018

  27. [35]

    Federated learning: Challenges, methods, and future directions

    Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37 0 (3): 0 50--60, 2020

    2020

  28. [36]

    Finding global homophily in graph neural networks when meeting heterophily

    Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. Finding global homophily in graph neural networks when meeting heterophily. In International Conference on Machine Learning, pp.\ 13242--13256. PMLR, 2022

    2022

  29. [37]

    Gslb: the graph structure learning benchmark

    Zhixun Li, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, et al. Gslb: the graph structure learning benchmark. Advances in Neural Information Processing Systems, 36, 2024

    2024

  30. [38]

    Towards deeper graph neural networks

    Meng Liu, Hongyang Gao, and Shuiwang Ji. Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp.\ 338--348, 2020

    2020

  31. [39]

    Federated graph neural networks: Overview, techniques and challenges

    Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, and Han Yu. Federated graph neural networks: Overview, techniques and challenges. arXiv preprint arXiv:2202.07256, 2022 a

    2022 arXiv

  32. [40]

    Federated graph neural networks: Overview, techniques, and challenges

    Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, and Han Yu. Federated graph neural networks: Overview, techniques, and challenges. IEEE Transactions on Neural Networks and Learning Systems, 2024

    2024

  33. [41]

    Towards unsupervised deep graph structure learning

    Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. Towards unsupervised deep graph structure learning. In Proceedings of the ACM Web Conference 2022, pp.\ 1392--1403, 2022 b

    2022

  34. [42]

    Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating

    Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, and Shirui Pan. Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 4516--4524, 2023 a

    2023

  35. [43]

    Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks

    Zhaowei Liu, Dong Yang, Yingjie Wang, Mingjie Lu, and Ranran Li. Egnn: Graph structure learning based on evolutionary computation helps more in graph neural networks. Applied Soft Computing, 135: 0 110040, 2023 b

    2023

  36. [44]

    Stfl: A temporal-spatial federated learning framework for graph neural networks

    Guannan Lou, Yuze Liu, Tiehua Zhang, and Xi Zheng. Stfl: A temporal-spatial federated learning framework for graph neural networks. arXiv preprint arXiv:2111.06750, 2021

    2021 arXiv

  37. [45]

    Revisiting heterophily for graph neural networks

    Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. Revisiting heterophily for graph neural networks. Advances in neural information processing systems, 35: 0 1362--1375, 2022

    2022

  38. [46]

    Learning to drop: Robust graph neural network via topological denoising

    Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang. Learning to drop: Robust graph neural network via topological denoising. In Proceedings of the 14th ACM international conference on web search and data mining, pp.\ 779--787, 2021

    2021

  39. [47]

    Learning disentangled representations for recommendation

    Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. Learning disentangled representations for recommendation. Advances in neural information processing systems, 32, 2019

    2019

  40. [48]

    Is homophily a necessity for graph neural networks? arXiv preprint arXiv:2106.06134, 2021

    Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. Is homophily a necessity for graph neural networks? arXiv preprint arXiv:2106.06134, 2021

    2021 arXiv

  41. [49]

    Demystifying structural disparity in graph neural networks: Can one size fit all? arXiv preprint arXiv:2306.01323, 2023

    Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. Demystifying structural disparity in graph neural networks: Can one size fit all? arXiv preprint arXiv:2306.01323, 2023

    2023 arXiv

  42. [50]

    Communication-efficient learning of deep networks from decentralized data

    Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp.\ 1273--1282. PMLR, 2017

    2017

  43. [51]

    Sgnn: A graph neural network based federated learning approach by hiding structure

    Guangxu Mei, Ziyu Guo, Shijun Liu, and Li Pan. Sgnn: A graph neural network based federated learning approach by hiding structure. In 2019 IEEE International Conference on Big Data (Big Data), pp.\ 2560--2568. IEEE, 2019

    2019

  44. [52]

    Query-driven active surveying for collective classification

    Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu. Query-driven active surveying for collective classification. In 10th international workshop on mining and learning with graphs, volume 8, pp.\ 1, 2012

    2012

  45. [53]

    Cosine similarity metric learning for face verification

    Hieu V Nguyen and Li Bai. Cosine similarity metric learning for face verification. In Asian conference on computer vision, pp.\ 709--720. Springer, 2010

    2010

  46. [54]

    Netprobe: a fast and scalable system for fraud detection in online auction networks

    Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. Netprobe: a fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th international conference on World Wide Web, pp.\ 201--210, 2007

    2007

  47. [55]

    Differentially private federated knowledge graphs embedding

    Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, and Jianxin Li. Differentially private federated knowledge graphs embedding. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp.\ 1416--1425, 2021

    2021

  48. [56]

    Multi-scale attributed node embedding

    Benedek Rozemberczki, Carl Allen, and Rik Sarkar. Multi-scale attributed node embedding. Journal of Complex Networks, 9 0 (2): 0 cnab014, 2021

    2021

  49. [57]

    The graph neural network model

    Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20 0 (1): 0 61--80, 2008

    2008

  50. [58]

    Collective classification in network data

    Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 29 0 (3): 0 93--93, 2008

    2008

  51. [59]

    Graph neural networks in particle physics

    Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant. Graph neural networks in particle physics. Machine Learning: Science and Technology, 2 0 (2): 0 021001, 2020

    2020

  52. [60]

    Graph structure learning with variational information bottleneck

    Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and S Yu Philip. Graph structure learning with variational information bottleneck. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pp.\ 4165--4174, 2022

    2022

  53. [61]

    Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns

    Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma. Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp.\ ...

    2021

  54. [62]

    Federated learning on non-iid graphs via structural knowledge sharing

    Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. Federated learning on non-iid graphs via structural knowledge sharing. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 9953--9961, 2023

    2023

  55. [63]

    Social influence analysis in large-scale networks

    Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.\ 807--816, 2009 a

    2009

  56. [64]

    Social influence analysis in large-scale networks

    Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.\ 807--816, 2009 b

    2009

  57. [65]

    Graph attention networks

    Petar Veli c kovi \'c , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li \`o , and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018

    2018

  58. [66]

    Confederated learning: Federated learning with decentralized edge servers

    Bin Wang, Jun Fang, Hongbin Li, Xiaojun Yuan, and Qing Ling. Confederated learning: Federated learning with decentralized edge servers. IEEE Transactions on Signal Processing, 71: 0 248--263, 2023

    2023

  59. [67]

    Am-gcn: Adaptive multi-channel graph convolutional networks

    Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei. Am-gcn: Adaptive multi-channel graph convolutional networks. In Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery & data mining, pp.\ 1243--1253, 2020

    2020

  60. [68]

    Nodeformer: A scalable graph structure learning transformer for node classification

    Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan. Nodeformer: A scalable graph structure learning transformer for node classification. Advances in Neural Information Processing Systems, 35: 0 27387--27401, 2022

    2022

  61. [69]

    Session-based recommendation with graph neural networks

    Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pp.\ 346--353, 2019

    2019

  62. [70]

    Graph information bottleneck

    Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. Graph information bottleneck. Advances in Neural Information Processing Systems, 33: 0 20437--20448, 2020 a

    2020

  63. [71]

    A comprehensive survey on graph neural networks

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32 0 (1): 0 4--24, 2020 b

    2020

  64. [72]

    Graph learning: A survey

    Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu. Graph learning: A survey. IEEE Transactions on Artificial Intelligence, 2 0 (2): 0 109--127, 2021

    2021

  65. [73]

    Graph neural networks in node classification: survey and evaluation

    Shunxin Xiao, Shiping Wang, Yuanfei Dai, and Wenzhong Guo. Graph neural networks in node classification: survey and evaluation. Machine Vision and Applications, 33 0 (1): 0 4, 2022

    2022

  66. [74]

    Federated graph classification over non-iid graphs

    Han Xie, Jing Ma, Li Xiong, and Carl Yang. Federated graph classification over non-iid graphs. Advances in Neural Information Processing Systems, 34: 0 18839--18852, 2021

    2021

  67. [75]

    Federated node classification over graphs with latent link-type heterogeneity

    Han Xie, Li Xiong, and Carl Yang. Federated node classification over graphs with latent link-type heterogeneity. In Proceedings of the ACM Web Conference 2023, pp.\ 556--566, 2023

    2023

  68. [76]

    How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018 a

    Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018 a

    2018 arXiv

  69. [77]

    Representation learning on graphs with jumping knowledge networks

    Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. Representation learning on graphs with jumping knowledge networks. In International conference on machine learning, pp.\ 5453--5462. PMLR, 2018 b

    2018

  70. [78]

    Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks

    Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. In 2022 IEEE International Conference on Data Mining (ICDM), pp.\ 1287--1292. IEEE, 2022

    2022

  71. [79]

    Diverse message passing for attribute with heterophily

    Liang Yang, Mengzhe Li, Liyang Liu, Chuan Wang, Xiaochun Cao, Yuanfang Guo, et al. Diverse message passing for attribute with heterophily. Advances in Neural Information Processing Systems, 34: 0 4751--4763, 2021

    2021

  72. [80]

    Graph pointer neural networks

    Tianmeng Yang, Yujing Wang, Zhihan Yue, Yaming Yang, Yunhai Tong, and Jing Bai. Graph pointer neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pp.\ 8832--8839, 2022

    2022

  73. [81]

    Revisiting semi-supervised learning with graph embeddings

    Zhilin Yang, William Cohen, and Ruslan Salakhudinov. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning, pp.\ 40--48. PMLR, 2016

    2016

  74. [82]

    Fedfm: Anchor-based feature matching for data heterogeneity in federated learning

    Rui Ye, Zhenyang Ni, Chenxin Xu, Jianyu Wang, Siheng Chen, and Yonina C Eldar. Fedfm: Anchor-based feature matching for data heterogeneity in federated learning. IEEE Transactions on Signal Processing, 2023

    2023

  75. [83]

    The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

    I-Cheng Yeh and Che-hui Lien. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert systems with applications, 36 0 (2): 0 2473--2480, 2009

    2009

  76. [84]

    Graph convolutional neural networks for web-scale recommender systems

    Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp.\ 974--983, 2018

    2018

  77. [85]

    Graph-revised convolutional network

    Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. Graph-revised convolutional network. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14--18, 2020, Proceedings, Part III, pp.\ 378--3...

    2020

  78. [86]

    Explainability in graph neural networks: A taxonomic survey

    Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. Explainability in graph neural networks: A taxonomic survey. IEEE transactions on pattern analysis and machine intelligence, 45 0 (5): 0 5782--5799, 2022

    2022

  79. [87]

    Graphsaint: Graph sampling based inductive learning method

    Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931, 2019

    1907 arXiv

  80. [88]

    Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation

    Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, and Lichao Sun. Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation. arXiv preprint arXiv:2203.09553, 2022

    2022 arXiv

  81. [89]

    Subgraph federated learning with missing neighbor generation

    Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu Ming Yiu. Subgraph federated learning with missing neighbor generation. Advances in Neural Information Processing Systems, 34: 0 6671--6682, 2021

    2021

  82. [90]

    Bayesian graph convolutional neural networks for semi-supervised classification

    Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Ustebay. Bayesian graph convolutional neural networks for semi-supervised classification. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pp.\ 5829--5836, 2019

    2019

  83. [91]

    Heterogeneous graph structure learning for graph neural networks

    Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. Heterogeneous graph structure learning for graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pp.\ 4697--4705, 2021 a

    2021

  84. [92]

    Data augmentation for graph neural networks

    Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. Data augmentation for graph neural networks. In Proceedings of the aaai conference on artificial intelligence, volume 35, pp.\ 11015--11023, 2021 b

    2021

  85. [93]

    Graphglow: Universal and generalizable structure learning for graph neural networks

    Wentao Zhao, Qitian Wu, Chenxiao Yang, and Junchi Yan. Graphglow: Universal and generalizable structure learning for graph neural networks. arXiv preprint arXiv:2306.11264, 2023

    2023 arXiv

  86. [94]

    Asfgnn: Automated separated-federated graph neural network

    Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, and Benyu Zhang. Asfgnn: Automated separated-federated graph neural network. Peer-to-Peer Networking and Applications, 14 0 (3): 0 1692--1704, 2021

    2021

  87. [95]

    Graph neural networks for graphs with heterophily: A survey

    Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082, 2022

    2022 arXiv

  88. [96]

    Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs

    Yizhen Zheng, He Zhang, Vincent Lee, Yu Zheng, Xiao Wang, and Shirui Pan. Finding the missing-half: Graph complementary learning for homophily-prone and heterophily-prone graphs. In International Conference on Machine Learning, pp.\ 42492--42505. PMLR, 2023

    2023

  89. [97]

    Graph neural networks: A review of methods and applications

    Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI open, 1: 0 57--81, 2020

    2020

  90. [98]

    Beyond homophily in graph neural networks: Current limitations and effective designs

    Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in neural information processing systems, 33: 0 7793--7804, 2020

    2020

  91. [99]

    Mixedad: A scalable algorithm for detecting mixed anomalies in attributed graphs

    Mengxiao Zhu and Haogang Zhu. Mixedad: A scalable algorithm for detecting mixed anomalies in attributed graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp.\ 1274--1281, 2020

    2020

  92. [100]

    Aligraph: A comprehensive graph neural network platform

    Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou. Aligraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730, 2019

    1902 arXiv

  93. [101]

    Federated learning of molecular properties with graph neural networks in a heterogeneous setting

    Wei Zhu, Jiebo Luo, and Andrew D White. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns, 3 0 (6), 2022

    2022

  94. [102]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.