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REVIEW 4 major objections 8 minor 159 references

Towards Data-centric Machine Learning on Directed Graphs: a Survey

T0 review · 4 major / 8 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This survey claims to be the first dedicated data-centric review of directed graph learning and organizes directed GNNs into message-passing, eigenpolynomial-based, and sequence-based frameworks.

desk verdict Useful survey of directed GNNs with a reasonable taxonomy, but the 'first comprehensive survey' claim is asserted without the search evidence to back it up. read the letter →

arxiv 2412.01849 v2 pith:C6MUBUVO submitted 2024-11-28 cs.LG cs.AIcs.DBcs.SI

Henan Sun , Xunkai Li , Daohan Su , Junyi Han , Rong-Hua Li , Guoren Wang This is my paper · ORCID
classification cs.LGcs.AIcs.DBcs.SI
keywords directedgraphsgraphneuralnetworksdata-centricmachinelearningsurveytaxonomyspectraltheorytransformersapplications
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

This survey sets out to establish that directed graphs deserve their own data-centric treatment in graph machine learning, and that the existing literature can be organized into a single map. Its central claim is that it is the first dedicated survey of directed GNNs, and its main instrument is a three-part taxonomy: message-passing models that propagate along directed edges, eigenpolynomial-based spectral models that filter asymmetric graph Laplacians, and sequence-based models that tokenize graphs for attention. The paper argues that preserving edge directionality raises the representational ceiling of GNNs and that graph construction and improvement, not just model design, limit performance. A sympathetic reader would take away a structured guide to the field, a data-centric vocabulary for diagnosing model failures, and a catalog of applications across more than ten domains.

What carries the argument

The central organizational device is the survey's taxonomy of directed GNNs, defined by the machinery a model uses to respect edge directionality. Message-passing models split propagation and aggregation along incoming and outgoing edges; eigenpolynomial-based models replace the symmetric Laplacian with directed filter bases, such as the personalized PageRank transition matrix or the magnetic Laplacian, and approximate spectral filters with polynomials; sequence-based models tokenize graphs into node or subgraph sequences and apply attention with directed positional encodings. The accompanying data-centric pipeline (graph construction, graph improvement, and GNN learning) and the three graph views do the work of reframing each method as a statement about data quality rather than only architecture.

What would settle it

Run a systematic literature search for a peer-reviewed survey published before this paper whose explicit subject is directed graph neural networks or directed graph representation learning; finding one with comparable scope would falsify the first-survey claim. To test the taxonomy, take a directed GNN method not listed in the paper and classify it: if its core mechanism cannot be assigned to message-passing, eigenpolynomial-based, or sequence-based without forcing, the claimed partition is not exhaustive.

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Extended reading notes

Core claim

The paper's core claim, stated on its own terms, is that no prior survey has specifically addressed directed GNNs, and that a data-centric review of this area is therefore both novel and needed. It proposes a taxonomy of directed GNNs into three principal frameworks: the message-passing framework, where propagation and aggregation operators are adapted to asymmetric edges; the eigenpolynomial-based framework, where graph filtering is done through filter bases and linear polynomials suited to non-symmetric and often complex-valued spectra; and the sequence-based framework, where graphs are tokenized into node or subgraph sequences and encoded with attention. The paper then revisits these methods from a data-centric perspective, reading each model through one of three graph views (topological, spectral, or sequential) and organizing data-improvement techniques into topological enhancement and node feature enhancement. Its further claim is that directed GNNs already serve more than ten application domains, and it closes with directions such as node feature denoising, scalability, robustness, and benchmarks for directed graphs.

Load-bearing premise

The load-bearing premise is that no earlier dedicated survey of directed GNNs exists, a claim asserted without a systematic search protocol; the taxonomy's exhaustive three-way partition is a second fragile premise.

Editorial extensions

If this is right

  • If the survey's map is right, practitioners can choose a directed GNN family by the view of the data they trust: topology-based message passing, spectral filtering, or sequence-based attention.
  • Preserving edge directionality should be treated as a data-quality decision, not a model detail; degrading a directed graph to undirected form caps what any downstream architecture can learn.
  • The data-centric pipeline gives a shared vocabulary for failure analysis: a directed GNN underperforms either because the graph was poorly constructed or because the representation ignores directionality, not solely because the architecture is weak.
  • The catalog of applications implies that remodeling a domain as directed, for instance treating drug-drug interaction as an edge-level directed prediction task, can open modeling options that undirected formulations miss.
  • The identified gaps (node feature denoising, subgraph-token sequences, and scalable, robust directed GNNs) point to the concrete next targets for the field.

Reading between the lines

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

  • A natural extension the paper leaves implicit is that the taxonomy could be applied to undirected GNNs by treating them as the special case where every edge is bidirectional, yielding a unified evaluation framework across directed and undirected graph learning.
  • If the data-centric premise is correct, benchmarks for directed graph learning should report graph-construction and augmentation choices alongside model architecture, since the survey implies that data quality bounds achievable performance.
  • The literature-priority claim is testable through a systematic citation and database search; even if an earlier directed-GNN survey appears, the three-framework taxonomy would remain a useful independent contribution.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 8 minor

Summary. The manuscript is a survey of directed graph neural networks (directed GNNs) framed from a data-centric perspective. It proposes a taxonomy with three frameworks—message-passing, eigenpolynomial-based, and sequence-based—and reviews representative directed GNN methods under each. It then re-examines these methods through three "graph data understanding" views (topological, spectral, sequence) and two improvement families (topological enhancement and node feature enhancement), catalogs applications across roughly a dozen domains, and closes with future research directions. The advertised contributions are: a claim to be the first comprehensive data-centric survey of directed GNNs, a novel taxonomy, a data-centric revisiting of existing methods, an industrial application overview, and a discussion of open challenges.

Significance. If the priority and coverage claims were established, this survey would be a useful organizational reference: it assembles a broad bibliography, gives compact formulas for spectral directed GNN methods, and connects directed GNN research to many application areas. The three-view data-centric framing is a reasonable pedagogical device, and the taxonomy, however imperfect, does give readers a structured entry point into a fragmented literature. The main weakness is that the survey's central novelty rests on an unverified "first comprehensive survey" claim, and the taxonomy's completeness is asserted rather than demonstrated. The paper is a competent descriptive survey, but it is not currently a definitive or fully auditable one.

major comments (4)
  1. [Section 1, Contributions bullet "Comprehensive Review"] The load-bearing novelty claim—"to the best of our knowledge, no survey has yet specifically addressed directed GNNs"—is not supported by any systematic evidence. The manuscript itself cites two data-centric graph learning surveys, [138] and [158], but does not state whether those surveys cover directed graphs, what their scopes are, or why they do not preempt the present contribution. No search protocol is reported: no databases, query set, date range, or inclusion/exclusion criteria. The authors should either provide a systematic scoping comparison against [138], [158], and other relevant graph-learning surveys, or soften the "first comprehensive" claim to a more specific and verifiable contribution.
  2. [Section 3 and Table 3] The taxonomy is asserted to encompass three principal frameworks, but the criteria for assigning a method to exactly one framework are never stated. The placement of LightDiC [70] under the message-passing framework is an example of the ambiguity: the text describes it as a digraph convolution founded on the magnetic Laplacian, which has a spectral basis, yet it is classified under "Propogator" in Table 3. To make the taxonomy auditable, the authors should define the defining properties of each framework and give a rule or argument for exhaustiveness and mutual exclusivity.
  3. [Section 3.2, Eq. (2)] Eq. (2) presents the graph spectral filter as x∗gθ = U gθ(Λ) Uᵀ x = Σ θᵢ Tᵢ(L͂)x, which presupposes that the filter basis has an orthogonal eigenbasis. That assumption holds for real-symmetric or normal matrices, but not for general directed graph operators. The paper itself later acknowledges this issue in the HoloNet discussion, where it notes that directed filter bases lacking the real-symmetric property cannot be decomposed into complete orthogonal eigenvectors. Eq. (2) should therefore be qualified as applying only to diagonalizable/normal filter bases, and the conjugate transpose should be used where the basis is complex.
  4. [Section 4.2.1 and Table 4] The checkmark-based classification in Table 4 is not consistently supported by the text, and the meaning of each column is not defined. For example, DiRW [103] is described in Section 4.2.1 as a diffusion-based enhancement that transforms the adjacency matrix into an augmented matrix, but Table 4 shows no improvement checkmarks for DiRW. Similar discrepancies make the "data-centric revisiting" contribution hard to audit. The table needs a legend, explicit column definitions, and a method-by-method justification for each checkmark.
minor comments (8)
  1. [Section 2.1] The definition of e_{ij} = (v_i, v_j) ∈ V is incorrect; the edge should be an element of E, not V. Also, "Through out" should be "Throughout".
  2. [Table 3 and Table 4] Several typos appear in the tables: "Propogator" should be "Propagator", and "NeurlPS" should be "NeurIPS".
  3. [Section 3.2] The text contains repeated typos: "comlex-value" should be "complex-value", "eigenpolynimial" should be "eigenpolynomial", and "Markiv chain" should be "Markov chain".
  4. [Section 5.1] Reference [126] is listed as "Traffic-GGNN" in Table 1 but as "Traffic-GCNN" in the text; the naming should be consistent.
  5. [Section 3.1 and Table 4] The method [49] is called "EDGNN" in the text and "edGNN" in Table 4; please use a single consistent name.
  6. [References] The Framelet-MagNet paper is duplicated as references [76] and [77], and the text uses both numbers for the same work.
  7. [Section 5.2] The sentence "the necessitates for forecasting the directed perspectives of these interactions" is ungrammatical and should be revised.
  8. [Table 1] The entry "DawnGNNz" appears to be a typo for "DawnGNN" (reference [31]).

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey organizes existing directed-GNN methods into a taxonomy and a data-centric viewpoint, but no claim is obtained by feeding its own output back as input.

full rationale

This is a survey paper, not a derivation or prediction pipeline, so the characteristic circularity patterns (self-definitional identities, fitted inputs renamed as predictions, or uniqueness theorems imported from the authors' own prior work) do not arise. The central 'first comprehensive survey' claim rests on an unverified literature-priority assertion, but that is a support gap rather than a circular argument. The three-way taxonomy (message-passing, eigenpolynomial-based, sequence-based) is descriptive and can be checked against the cited methods; it does not reduce to the authors' own models. The authors do highlight their own works (LightDiC [70], ADPA [105], DiRW [103]) and even suggest building on DiRW [103] in future directions, but these self-citations are not load-bearing for the survey's organizational claims and no theorem or benchmark result is justified solely by citing the same authors. Per the reviewing rule, I flag the unsupported 'no survey has yet specifically addressed directed GNNs' statement as a correctness risk, but not as circularity, because the paper does not derive that statement from its own conclusions. No equation in the paper takes a fitted value from one part and presents it as an independent prediction elsewhere, and no category is defined in terms of the conclusion it is supposed to support. The finding is therefore no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey introduces no fitted constants, no new theoretical objects, and no derived equations. The only 'new' contribution is the taxonomy, which is an organizational choice rather than an empirical or formal claim.

assumptions (3)
  • domain assumption The three-way taxonomy (message-passing, eigenpolynomial-based, sequence-based) is a meaningful, near-exhaustive partition of directed GNN methods.
    Section 3 states the taxonomy without a formal criterion or completeness argument; if the partition is arbitrary or overlapping, the survey's organizational claim weakens.
  • domain assumption No prior comprehensive survey of directed GNNs exists.
    Section 1 ('to the best of our knowledge, no survey has yet specifically addressed directed GNNs') asserts priority without a systematic literature search; the survey's novelty rests on this.
  • domain assumption The descriptions of the cited models are faithful to their original papers.
    No re-implementation or cross-checking is performed; accuracy relies on the authors' reading of 160 references.

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Cite this review

Pith. "Pith review of Towards Data-centric Machine Learning on Directed Graphs: a Survey." pith.science (2026). https://pith.science/paper/C6MUBUVO

@misc{pith2026241201849,
  author       = {Pith},
  title        = {Pith review of: Towards Data-centric Machine Learning on Directed Graphs: a Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C6MUBUVO}},
  note         = {Machine review of arXiv:2412.01849}
}
read the original abstract

In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them into undirected formats and emphasizes model designs. This approach is inherently limited in real-world applications due to the unavoidable information loss in simple undirected graphs and the model optimization challenges that arise when exceeding the upper bounds of this sub-optimal data representational capacity. As a result, there has been a shift toward data-centric methods that prioritize improving graph quality and representation. Specifically, various types of graphs can be derived from naturally structured data, including heterogeneous graphs, hypergraphs, and directed graphs. Among these, directed graphs offer distinct advantages in topological systems by modeling causal relationships, and directed GNNs have been extensively studied in recent years. However, a comprehensive survey of this emerging topic is still lacking. Therefore, we aim to provide a comprehensive review of directed graph learning, with a particular focus on a data-centric perspective. Specifically, we first introduce a novel taxonomy for existing studies. Subsequently, we re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation. It demonstrates that a deep understanding of directed graphs and their quality plays a crucial role in model performance. Additionally, we explore the diverse applications of directed GNNs across 10+ domains, highlighting their broad applicability. Finally, we identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.

Figures

Figures reproduced from arXiv: 2412.01849 by the authors.

Figure 1
Figure 1. The pipeline of data-centric graph machine learning. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

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Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.