SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

Chao Gao; Keke Tang; Le Cheng; Peican Zhu; Yangming Guo; Zhen Wang

REVIEW 3 major objections 7 minor 40 references

SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

T0 review · 3 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read SourceDetMamba claims that replaying hypergraph rumor snapshots in reverse order through a Mamba state space model with a graph-aware state update detects rumor sources more accurately and faster than state-of-the-art baselines.

desk verdict The architecture is a plausible new combination, but the empirical claim is undermined by an input-information asymmetry: the model gets infection timestamps and the baselines apparently don't, which could explain the entire reported margin. read the letter →

arxiv 2505.12910 v2 pith:HEYYDJR2 submitted 2025-05-19 cs.SI cs.AI

Le Cheng , Peican Zhu , Yangming Guo , Chao Gao , Zhen Wang , Keke Tang This is my paper · ORCID

classification cs.SIcs.AI

keywords sourcedetectionhypergraphsstatespacemodelsMambarumorpropagationsequentialsnapshotslocalizationgraphneuralnetworks

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

SourceDetMamba claims that rumor sources can be located by replaying the observed propagation backward: feed hypergraph snapshots of an ongoing rumor to a state space model in reverse chronological order, and the model learns the dynamics that led to the observed state. The paper extends the Mamba architecture with a graph-aware state update, so each node's hidden state is refined simultaneously by the temporal sequence and by messages from its hyperedges. On eight datasets with synthetic cascades, the method reports 10–23% higher accuracy than propagation-aware baselines, 25–35% higher than user-state baselines, and about twice the accuracy of source-centrality methods, while keeping runtime low. The value, if the claim holds, is a source-detection approach that avoids fitting a propagation model as a separate error-prone step.

What carries the argument

The load-bearing mechanism is the graph-aware state update inside a Mamba state space model. Mamba is a selective state space model that processes sequences in linear time by maintaining a hidden state; here the hidden state of each node is not updated only from the sequence input $B x_t$ and the recurrent term $A h_{t-1}$, but also gets a topological term $H D_E^{-1} \Omega H^T D_V^{-1} h_{t-1}$ that propagates and refines states through hyperedges in two stages. This term is what lets the model combine temporal dependencies with hypergraph structure, and the learned hyperedge weights $\Omega$ let different hyperedges contribute according to their role in the spread.

What would settle it

Train SourceDetMamba on the paper's synthetic cascades and evaluate it on real social-media rumor traces with independently verified ground-truth sources using the same three snapshots; if its F-Score no longer beats GIN-SD and IVGD by the reported margins, the central claim that reverse-order snapshot learning generalizes to real propagation dynamics is falsified.

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Extended reading notes

Core claim

The central claim is that SourceDetMamba detects rumor sources more accurately than existing methods because it learns the propagation process directly from sequential hypergraph snapshots rather than from a fitted model or a single snapshot. Snapshots are fed in reverse order, so the first element the model sees is the most recent state and the last is the earliest, and the final output element is used to identify sources. To keep the topology from being lost, the paper adds a graph-aware state update: each node's state is updated as $h_t = A h_{t-1} + B x_t + H D_E^{-1} \Omega H^T D_V^{-1} h_{t-1}$, where the added term aggregates neighbor states through node-to-hyperedge and hyperedge-to-node passes, with hyperedge weights learned from the current state. On eight datasets, the method reports consistently higher ACC, F-Score, and AUC than all baselines, with the largest claimed gains over propagation-aware methods.

Load-bearing premise

The reported gains depend on cascades generated by the paper's synthetic heterogeneous independent cascade model—5% random sources, $p_i \sim U(0,0.5)$, high-order thresholds $p_\Delta = 0.3$ times the informed fraction, snapshots at 10/20/30% informed—faithfully representing real rumor dynamics; if real cascades have different thresholds, recovery, or observation noise, the reversal-learning signal may disappear.

Editorial extensions

If this is right

On cascade data generated by similar hypergraph contagion models, SourceDetMamba should keep beating propagation-fitting baselines across the eight datasets evaluated, since the multi-snapshot reverse-order signal is the source of the gain.
Removing any component of the design—the sequential snapshots, the positional encodings, or the graph-aware state update—degrades performance, so each part is doing load-bearing work.
The method stays accurate under SI, SIS, SIR, and IC diffusion models, with a slight drop when sources can recover, so the reversal signal is not tied to one specific spreading rule.
Because the model reads only snapshots and outputs source scores directly, it avoids the extra error of first fitting a propagation model to the observed state.
Snapshot interval matters: intervals too small miss dynamics and intervals too large over-weight non-source influence, so choosing an appropriate observation timescale is part of the method's success.

Reading between the lines

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

A testable extension is to train on synthetic cascades and evaluate on real propagation traces with verified ground-truth sources; if the reported gains shrink, the synthetic-cascade assumption would be the bottleneck.
The same reversal-learning recipe could apply to other inverse problems on temporal hypergraphs, such as identifying the first adopters in opinion dynamics or the origin of a computer worm, where reverse-ordered snapshots are available.
The graph-aware state update is a generic plug-in for Mamba-style models, so it could be used not just for source detection but for any sequential task on hypergraphs that needs topology-aware hidden states.
Because hyperedge weights are learned per state, the model may implicitly identify which group interactions carried the rumor, giving a by-product explanation of the main transmission channels—something the paper does not test.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: SourceDetMamba is a sensible new combination — reverse-order sequential hypergraph snapshots fed into Mamba with a graph-aware state update (Eq. 16) — and the ablations show the design choices matter. But the central numerical claim does not hold up as presented.

The stress-test note you passed me lands. Section 4.1 gives the model X2_i = t_{v_i}, the exact infection timestamp, for every informed node. Section 5.1 says propagation starts with 5% randomly selected sources at t=0. So the true source set is exactly the set of informed nodes with timestamp 0. A trivial threshold on that feature solves the task or at least gives a very strong prior. The baseline descriptions in Section 2.1 and the clique-expansion note in Section 5.1 never say the baselines receive timestamps; they get user states or fitted diffusion models. If that asymmetry is real, the claimed 10-23% accuracy advantage is not evidence for the proposed graph-aware Mamba mechanism. The paper never ablates the X2 feature, so we cannot tell how much of the gain is from the brute-force timestamp signal.

None of this kills the architecture. The graph-aware state update with hyperedge aggregation and learned hyperedge weights is a reasonable extension of Mamba to sequential hypergraphs, and the reverse-order input idea is natural. The ablation section is informative: removing node communication or edge weights hurts, which supports the mechanism's role. The runtime comparison is fine as far as it goes.

Other soft spots, in proportion: no code, no seeds, no error bars — every table entry is a single run on synthetic cascades. The convolution-kernel claim (y = X' * K) is not reconciled with the graph-aware update, which adds an extra term; that is a minor presentation gap. The class-balance weight xi in Eq. 17 is standard ground-truth reweighting, not a circularity.

Who is this for? People working on source detection or temporal graph learning might read the methods section for the architecture idea. I would not cite the reported numbers until the evaluation is fixed. The paper deserves a serious referee: the idea is testable, and the authors are likely capable of shipping code. The right outcome is major revision asking for feature-matched baselines, an ablation of X2, and code release.

Referee Report

3 major / 7 minor

Summary. The paper proposes SourceDetMamba, a state-space-model-based method for source detection in sequential hypergraph snapshots. Hypergraph snapshots are embedded with user states, diffusion timestamps, and Laplacian positional encodings, then fed in reverse chronological order into Mamba blocks. A graph-aware state update mechanism propagates node states through hyperedges, and the final sequence element is used for per-node source classification. Experiments on eight datasets with synthetic hypergraph propagation report consistent and substantial improvements over nine baselines in ACC, F-Score, and AUC, plus an ablation study of the main components.

Significance. If the empirical claims hold, the paper provides a meaningful step toward using linear-complexity sequence models for temporal source localization on higher-order networks. The idea of reverse-order snapshot sequences and a graph-aware state update is interesting and the ablations are largely consistent. However, the evaluation rests on a potentially privileged input feature (exact infection timestamps), and the results are reported without variance or code release, so the magnitude of the stated advantage over baselines is not yet verified. The paper also contains a technical inconsistency in its description of the state-space convolution kernel.

major comments (3)

[§4.1 Eq. (4); §5.1; §5.2] The diffusion timestamp feature X2_i = t_{v_i} is given only to SourceDetMamba for every informed node. In the Section 5.1 simulation, propagation starts at t=0 with 5% randomly selected sources, so the true sources are exactly the informed nodes with t_{v_i}=0. A trivial rule that flags these nodes would solve the task. The paper does not state that the nine baselines receive this timestamp feature; Section 5.1 only mentions that hypergraph snapshots are clique-expanded before being applied to baselines. Table 2 ablates positional encodings and the graph-aware state update, but never ablates the timestamp feature, so the 10%-23% accuracy advantage claimed in Section 5.2 may stem from an input-information asymmetry rather than from the proposed Mamba mechanism. Please either remove the timestamp feature from SourceDetMamba, provide equivalent temporal information to all baselines, or add an ablation 'w/o timestamps' and a comparison against a threshold-based rule (informed and t=0) to establish that the reported gains survive without this privileged information.
[§5.1; Table 3] All evaluations are on synthetic cascades generated by a heterogeneous independent cascade model with 5% random sources and p_i ~ U(0,0.5). The central claim of 'consistently outperforms state-of-the-art approaches' therefore depends on the faithfulness of this simulation to real rumor dynamics on hypergraphs. The paper does not justify that the reversal-learning signal remains strong under different thresholds, recovery, or observation noise. Table 3 tests SIR/SIS but only for SourceDetMamba, not for baselines, and no variance is reported. Please add experiments with observation noise or partial observability, report baseline performance under SIR/SIS, and explicitly discuss this synthetic-data limitation in the text.
[§4.2] The paragraph states that 'by utilizing convolution kernel K = (CB, CAB, ..., CA^k B, ...), we achieve the sequence-to-sequence transformation through y = X' * K'. This convolution representation is valid for a linear time-invariant (LTI) state space model, but the paragraphs immediately before introduce Mamba's selective SSM, where B, C, and Δ are input-dependent functions, so a fixed convolution kernel does not exist. The description as written is internally inconsistent. Please correct this to state that the selective-scan (input-dependent) formulation is used, and that the convolution form applies only in the non-selective LTI case, or remove the fixed-K statement entirely.

minor comments (7)

[§5.1; Table 1] No standard deviations, confidence intervals, or repeated runs are reported. Since neural network training is stochastic, please report mean and standard deviation over at least five random seeds for the proposed method and all baselines.
[§5.1] The eight datasets are standard machine learning benchmark datasets used as hypergraph structures; calling them 'real-world datasets' for rumor source detection is misleading because the cascades are simulated. Please clarify that the evaluation is synthetic propagation over real-world hypergraph structures.
[§3; §4.2] The symbol Ω is overloaded: it denotes the given hyperedge weight matrix in Eqs. (1) and (9) but also the learned hyperedge weights in Eqs. (15)-(16). Please use a different symbol (e.g., Ω̂) for the learned weights.
[§4.2] In 'y = X′ ∗ K', the star operator is not defined; please specify that it denotes temporal convolution along the sequence dimension, to avoid confusion with graph convolution used elsewhere.
[§4.3] The sentence 'for a sample x with corresponding label z, L(x,z) = −log(x) × z' is unclear; please rewrite using standard cross-entropy notation for binary classification.
[§5.3; Table 3] The robustness experiment with SIR/SIS models reports only SourceDetMamba results. For a fair robustness claim, please also report the performance of at least the strongest baselines under these diffusion models.
[§5.2; Fig. 4] The runtime comparison appears to be on a single device without timing details (e.g., whether timings include data loading) or error bars. Please specify the exact hardware/software configuration and the number of independent runs.

Circularity Check

1 steps flagged · score 8.0 of 10

SourceDetMamba's input feature X2_i is the exact infection timestamp; in the synthetic evaluation sources are precisely the nodes with t=0, so the SOTA result reduces to thresholding the input.

self definitional [Section 4.1, Eq. (4); Section 5.1, Implementation; Section 3, Propagation Process on Hypergraphs]
"Diffusion information X2 i is defined as: X2 i = tvi, vi∈G+ ti; −1, otherwise. (4) ... The propagation process is triggered by randomly selecting 5% of the nodes as sources. ... the initially uninformed source nodes transition to the informed state at t = 0, triggering the spread of information."

The ground-truth source set in the synthetic benchmark is, by construction, the nodes with infection time t=0: Section 3 states sources transition to informed at t=0, and Section 5.1 triggers propagation by randomly selecting 5% of nodes as sources. Eq. (4) gives every informed node's exact infection time t_{v_i} as input feature X2_i. Therefore the target label (is source) is a deterministic function of the input feature (X2_i == 0) up to learned thresholding; no propagation-pattern learning or graph-aware state update is needed to solve the task. Table 2 ablates positional encodings and the graph-aware update but never removes X2_i, so the reported 10%-23% SOTA margin in Section 5.2 is consistent with input-information asymmetry rather than with the proposed mechanism.

full rationale

The paper is otherwise empirically self-contained: no fitted parameter is renamed as a prediction, and the citations to the authors' prior GIN-SD and to Mamba are not load-bearing for the architecture's derivation. However, the central empirical claim is compromised because the timestamp feature in Eq. (4) encodes the answer in the synthetic setup: sources are exactly the nodes with t=0. The 'reverse-order Mamba learns propagation patterns' explanation is therefore circular with respect to the evaluation design. If the timestamp feature were removed or the baselines were also given it, the comparison could still support the method, but as reported the SOTA advantage is forced by construction. Score 8 reflects that the main empirical result reduces to the input feature by definition.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central method contributes no closed-form derivation; it is a learned architecture. What it borrows is standard Mamba and hypergraph-convolution machinery. What it postulates is that the chosen synthetic cascade model and snapshot schedule produce a learnable reversal signal for sources, and that the infected-subgraph Laplacian positional encoding carries source-relevant structure. These domain assumptions, plus hand-set simulation constants, carry most of the explanatory weight in the reported benchmarks.

free parameters (6)

source fraction = 5% (0.05)
In Section 5.1, sources are randomly selected as 5% of nodes; the task difficulty and class balance depend on this choice.
low-order propagation probability p_i = Uniform(0, 0.5)
Section 5.1 simulates low-order spreading with each node's infection probability drawn from U(0,0.5); this defines the synthetic dynamics the model learns to invert.
high-order transition probability coefficient = 0.3
Section 5.1 sets p_delta = 0.3 * (|e intersect v+| / |e|), a hand-chosen constant controlling high-order contagion strength.
snapshot capture thresholds = 10%, 20%, 30% informed
Section 5.1 captures snapshots at 10%, 20%, and 30% informed nodes; the number and spacing of snapshots is a free experimental choice that affects reversal-learning signal.
loss imbalance weight xi = |s| / (n - |s|)
Equation (17) defines xi from class sizes; it is not fit, but it is a modeling choice that shapes the optimization objective.
regularization and optimization hyperparameters = lr=1e-3, weight decay=1e-5, state size=128, two Mamba layers
Section 5.1 lists these; they are hand-set and could influence the reported margins.

assumptions (5)

domain assumption Rumor propagation on the tested hypergraphs is adequately described by the heterogeneous independent cascade model with the stated low- and high-order probabilities.
Section 5.1 generates all training and test data with this model; the model's success on that data says nothing about other propagation mechanisms without transfer evidence.
domain assumption The observed snapshots (10%, 20%, 30% informed) contain enough information to uniquely or approximately identify the source set.
Section 3 defines source detection from snapshots; if the reversal problem is underdetermined, learning from these snapshots cannot recover true sources.
domain assumption Reverse chronological snapshot order provides a learnable signal of propagation dynamics.
Section 4.2 feeds snapshots in reverse order; this is the core inductive bias and is not proven, only ablated.
standard math Mamba state-space recurrence (Eq. 10) and hypergraph convolution (Eq. 9) from cited prior work are accepted as given.
The method relies on the standard SSM discretization, ZOH update, and HGNN message passing as background.
domain assumption Laplacian positional encodings of the infected subgraph are informative about source location.
Equations (5)-(7) define positional features using eigenvectors of the infected subgraph Laplacian; the choice is motivated by source centrality but is not independently validated.

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Cite this review

Pith. "Pith review of SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs." pith.science (2026). https://pith.science/paper/HEYYDJR2

@misc{pith2026250512910,
  author       = {Pith},
  title        = {Pith review of: SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HEYYDJR2}},
  note         = {Machine review of arXiv:2505.12910}
}

read the original abstract

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

Figures

Figures reproduced from arXiv: 2505.12910 by the authors.

**Figure 2.** Framework of SourceDetMamba. Starting with hypergraph propagation snapshots, node features are embedded and refined via [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Visualization of source detection results on NTU2012. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Comparison of runtime and F-Score across baseline meth [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Performance of SourceDetMamba under different propa [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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