pith. sign in

arxiv: 2605.26290 · v1 · pith:76XQ2LMJnew · submitted 2026-05-25 · 💻 cs.LG

Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks

Derek Regier , Andrew Polyak , Aresh Dadlani , Khosro Salmani This is my paper

Pith reviewed 2026-06-29 22:32 UTC · model grok-4.3

classification 💻 cs.LG
keywords temporal signed networkslink predictionsigned graph neural networkshistorical contextdynamic graphsLSTMtemporal attention
0
0 comments X

The pith

A modular framework adds historical context to static signed GNNs to improve link prediction on evolving signed networks.

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

The paper aims to show that static signed graph neural networks can be extended for temporal signed networks by inserting a Historical Context Integration Module. This module combines recency-aware weighting, LSTM modeling of embedding changes over time, and multi-head attention to blend short-term and long-term signed interactions. The blended historical information is merged with current node states using either uniform or per-node weights. Experiments on Bitcoin trust networks, Reddit data, and synthetic small-world graphs report consistent gains over the unchanged baseline. If correct, the result means link prediction in domains with both positive and negative relations can use existing signed GNN code with an added temporal layer rather than requiring entirely new architectures.

Core claim

The central claim is that a modular temporal enhancement framework, built around the Historical Context Integration Module, integrates historical node representations into otherwise static signed GNNs through learnable recency weighting, LSTM trajectory modeling, and temporal attention, and that fusing this history via global or node-adaptive schemes produces statistically significant gains in link prediction accuracy on temporal signed networks.

What carries the argument

The Historical Context Integration Module (HCIM), which processes past node embeddings with recency weighting, LSTM, and multi-head attention before fusing them with current embeddings to capture time-varying signed dynamics.

If this is right

  • The same module can be attached to different signed GNN backbones without redesigning them.
  • Performance gains appear on both real datasets such as Bitcoin OTC and Alpha and on synthetic temporal small-world networks.
  • Interpretability features of the base model, such as in SE-SGformer, remain intact after the addition.
  • Both global and node-specific weighting options allow the framework to adapt to heterogeneous temporal patterns across nodes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same module could be tested on temporal signed graphs arising in citation or collaboration networks where positive and negative edges also evolve.
  • If the fusion step proves unstable on very large graphs, replacing the LSTM with a simpler recurrence might preserve gains while lowering compute cost.
  • The results imply that signed balance constraints interact with time, so purely static signed models may systematically underfit in any domain with repeated interactions.

Load-bearing premise

Fusing historical node states with current ones through weighting will capture signed temporal dynamics without introducing instability or harming performance on non-temporal features.

What would settle it

Apply the enhanced model and its static baseline to a fresh temporal signed network dataset and observe no statistically significant accuracy difference, or find that removing the LSTM or attention components leaves performance unchanged.

Figures

Figures reproduced from arXiv: 2605.26290 by Andrew Polyak, Aresh Dadlani, Derek Regier, Khosro Salmani.

Figure 1
Figure 1. Figure 1: Our proposed module for integrating historical context into SGNN [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Degree distribution statistics across evaluated datasets. The logarithmic [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Edge concentration in the full Reddit Hyperlink dataset. A small [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks. While graph neural networks (GNNs) perform well for static or unsigned link prediction, effective learning in temporal signed graphs remains challenging due to the interaction of signed relations, evolving structure, and balance-theoretic constraints. To address this gap, we propose a \emph{modular} temporal enhancement framework for signed GNNs that integrates historical context into otherwise static architectures. The framework introduces a Historical Context Integration Module (HCIM) that combines learnable recency-aware temporal weighting, LSTM-based embedding trajectory modeling, and multi-head temporal attention to capture both short- and long-term signed interaction dynamics. Historical information is fused with current node representations using either global or node-adaptive weighting, allowing the architecture-agnostic framework to accommodate heterogeneous temporal behaviors. We instantiate the approach on the Self-Explainable Signed Graph Transformer (SE-SGformer), preserving interpretability while extending it with temporal awareness. Experiments on real-world and synthetic TSNs, including Bitcoin OTC, Bitcoin Alpha, Reddit, and small-world network models, demonstrate consistent and statistically significant improvements over the static baseline.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a modular temporal enhancement framework for signed graph neural networks to perform dynamic link prediction on temporal signed networks (TSNs). The core contribution is the Historical Context Integration Module (HCIM), which augments static signed GNN architectures with learnable recency-aware temporal weighting, LSTM-based embedding trajectory modeling, and multi-head temporal attention. Historical context is fused with current node representations via either global or node-adaptive weighting. The framework is instantiated on the Self-Explainable Signed Graph Transformer (SE-SGformer) while preserving its interpretability. Experiments on real-world datasets (Bitcoin OTC, Bitcoin Alpha, Reddit) and synthetic small-world networks report consistent and statistically significant improvements over the corresponding static baselines.

Significance. If the experimental results hold, the work provides a practical, architecture-agnostic way to extend existing signed GNNs to temporal settings, which is relevant for applications involving evolving cooperative and adversarial relations. The modular design and the option for node-adaptive fusion are positive features that accommodate heterogeneous temporal behaviors without requiring full architectural redesign. The explicit preservation of interpretability in the SE-SGformer instantiation is a strength worth noting.

minor comments (2)
  1. [Abstract] Abstract: the statement that improvements are 'statistically significant' would benefit from a brief indication of the evaluation metrics (e.g., AUC, F1) and the number of independent runs or statistical test used, even at the abstract level.
  2. [Method (HCIM description)] The description of the fusion step (global vs. node-adaptive weighting) is clear in principle but would be strengthened by an explicit equation or pseudocode block showing how the two weighting schemes are computed and applied to the node embeddings.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the manuscript, the accurate summary of the HCIM framework, and the recommendation for minor revision. We appreciate the recognition of the architecture-agnostic design, node-adaptive fusion option, and preservation of interpretability as strengths.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes an additive modular framework (HCIM) that augments existing static signed GNNs with standard temporal components (recency weighting, LSTM trajectory modeling, multi-head attention) and fuses them via global or node-adaptive weights. The central claim rests on empirical evaluation against external real-world and synthetic datasets (Bitcoin OTC, Alpha, Reddit, small-world models), with no equations, self-definitions, or fitted-input predictions shown that reduce the reported gains to the inputs by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are present in the text. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no specific free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.1-grok · 5758 in / 1094 out tokens · 47190 ms · 2026-06-29T22:32:52.979623+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

49 extracted references · 6 canonical work pages · 5 internal anchors

  1. [1]

    Temporal Graph Networks for Deep Learning on Dynamic Graphs

    E. Rossi, B. Chamberlain, F. Frasca, D. Eynard, F. Monti, and M. Bronstein, “Temporal graph networks for deep learning on dynamic graphs,” pp. 1–16, Jun. 2020. [Online]. Available: https://arxiv.org/abs/2006.10637

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  2. [2]

    DyRep: Learning representations over dynamic graphs,

    R. S. Trivedi, M. Farajtabar, P. Biswal, and H. Zha, “DyRep: Learning representations over dynamic graphs,” inProc. Int. Conf. Learn. Repre- sent. (ICLR), May 2019, pp. 1–25

    2019

  3. [3]

    Predicting dynamic embedding trajectory in temporal interaction networks,

    S. Kumar, X. Zhang, and J. Leskovec, “Predicting dynamic embedding trajectory in temporal interaction networks,” inProc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), Aug. 2019, pp. 1269–1278

    2019

  4. [4]

    Edge weight prediction in weighted signed networks,

    S. Kumar, F. Spezzano, V . S. Subrahmanian, and C. Faloutsos, “Edge weight prediction in weighted signed networks,” inProc. IEEE Int. Conf. Data Min. (ICDM), Dec. 2016, pp. 221–230

    2016

  5. [5]

    REV2: Fraudulent user prediction in rating platforms,

    S. Kumar, B. Hooi, D. Makhija, M. Kumar, C. Faloutsos, and V . Sub- rahmanian, “REV2: Fraudulent user prediction in rating platforms,” in Proc. ACM Int. Conf. Web Search Data Min. (WSDM), Feb. 2018, pp. 333–341

    2018

  6. [6]

    Community interaction and conflict on the web,

    S. Kumar, W. L. Hamilton, J. Leskovec, and D. Jurafsky, “Community interaction and conflict on the web,” inProc. World Wide Web Conf. (WWW), Apr. 2018, pp. 933–943

    2018

  7. [7]

    On the notion of balance of a signed graph

    F. Harary, “On the notion of balance of a signed graph.”Mich. Math. J., vol. 2, no. 2, pp. 143–146, Jan. 1953

    1953

  8. [8]

    Co-evolution of viral processes and structural stability in signed social networks,

    T. Kalimzhanov, A. H. A. Khamseh’i, A. Dadlani, M. S. Kumar, and A. Khonsari, “Co-evolution of viral processes and structural stability in signed social networks,”IEEE Trans. Knowl. Data Eng., vol. 35, no. 8, pp. 7809–7814, Aug. 2023

    2023

  9. [9]

    Self-explainable graph transformer for link sign prediction,

    L. Li, J. Liu, X. Ji, M. Wang, and Z. Zhang, “Self-explainable graph transformer for link sign prediction,” inProc. AAAI Conf. Artif. Intell., Feb. 2025, pp. 12 084–12 092

    2025

  10. [10]

    Temporal networks,

    P. Holme and J. Saram ¨aki, “Temporal networks,”Phys. Rep., vol. 519, no. 3, pp. 97–125, Oct. 2012

    2012

  11. [11]

    Signed networks in social media,

    J. Leskovec, D. Huttenlocher, and J. Kleinberg, “Signed networks in social media,” inProc. SIGCHI Conf. Hum. Factor. Comput. Syst. (CHI), Apr. 2010, pp. 1361–1370

    2010

  12. [12]

    A compre- hensive survey of dynamic graph neural networks: Models, frameworks, benchmarks, experiments and challenges,

    Z. Feng, R. Wang, T. Wang, M. Song, S. Wu, and S. He, “A compre- hensive survey of dynamic graph neural networks: Models, frameworks, benchmarks, experiments and challenges,”IEEE Trans. Knowl. Data Eng., vol. 38, no. 1, pp. 26–46, Jan. 2026. 11

    2026

  13. [13]

    Attitudes and cognitive organization,

    F. Heider, “Attitudes and cognitive organization,”J. Psychol., vol. 21, pp. 107–112, 1946

    1946

  14. [14]

    E-LSTM-D: A deep learning framework for dynamic network link prediction,

    J. Chen, J. Zhang, X. Xu, C. Fu, D. Zhang, Q. Zhang, and Q. Xuan, “E-LSTM-D: A deep learning framework for dynamic network link prediction,”IEEE Trans. Syst., Man, Cybern., vol. 51, no. 6, pp. 3699– 3712, Jun. 2021

    2021

  15. [15]

    GC-LSTM: graph convolution embedded lstm for dynamic network link prediction,

    J. Chen, X. Wang, and X. Xu, “GC-LSTM: graph convolution embedded lstm for dynamic network link prediction,”Appl. Intell., vol. 52, no. 7, pp. 7513–7528, Sep. 2021

    2021

  16. [16]

    dynnode2vec: Scalable dy- namic network embedding,

    S. Mahdavi, S. Khoshraftar, and A. An, “dynnode2vec: Scalable dy- namic network embedding,” inProc. IEEE Int. Conf. Big Data (Big Data), Dec. 2018, pp. 3762–3765

    2018

  17. [17]

    Dynamic graph embedding via LSTM history tracking,

    S. Khoshraftar, S. Mahdavi, A. An, Y . Hu, and J. Liu, “Dynamic graph embedding via LSTM history tracking,” inProc. IEEE Int. Conf. Data Sci. Adv. Analytics (DSAA), Oct. 2019, pp. 119–127

    2019

  18. [18]

    Bi-DNE: bilayer evo- lutionary pattern preserved embedding for dynamic networks,

    X. Gu, X. Chen, M. Yang, Y . Du, and M. Tang, “Bi-DNE: bilayer evo- lutionary pattern preserved embedding for dynamic networks,”Complex Intell. Syst., vol. 10, no. 3, pp. 3763–3788, Feb. 2024

    2024

  19. [19]

    Inductive representation learning on temporal graphs.arXiv preprint arXiv:2002.07962, 2020

    D. Xu, C. Ruan, E. K ¨orpeoglu, S. Kumar, and K. Achan, “Inductive representation learning on temporal graphs,”ArXiv, vol. abs/2002.07962,

    work page arXiv 2002

  20. [20]

    Available: https://api.semanticscholar.org/CorpusID: 211171395

    [Online]. Available: https://api.semanticscholar.org/CorpusID: 211171395

  21. [21]

    DySAT: Deep neural representation learning on dynamic graphs via self-attention networks,

    A. Sankar, Y . Wu, L. Gou, W. Zhang, and H. Yang, “DySAT: Deep neural representation learning on dynamic graphs via self-attention networks,” inACM Int. Conf. Web Search Data Min (WSDM), Jan. 2020, pp. 519–527

    2020

  22. [22]

    Learnable spatial- temporal positional encoding for link prediction,

    K. Tieu, D. Fu, Z. Li, R. Maciejewski, and J. He, “Learnable spatial- temporal positional encoding for link prediction,” inProc. ACM Int. Conf. Mach. Learn. (ICML), Jul. 2025, pp. 59 570–59 597

    2025

  23. [23]

    Improving temporal link prediction via temporal walk matrix projection,

    X. Lu, L. Sun, T. Zhu, and W. Lv, “Improving temporal link prediction via temporal walk matrix projection,” inProc. ACM Adv. Neural Inf. Process. Syst. (NeurIPS), Dec. 2024, pp. 141 153–141 182

    2024

  24. [24]

    TREND: Temporal event and node dynamics for graph representation learning,

    Z. Wen and Y . Fang, “TREND: Temporal event and node dynamics for graph representation learning,” inProc. ACM Web Conf. (WWW), Apr. 2022, pp. 1159–1169

    2022

  25. [25]

    Inductive represen- tation learning in temporal networks via causal anonymous walks,

    Y . Wang, Y . Chang, Y . Liu, J. Leskovec, and P. Li, “Inductive represen- tation learning in temporal networks via causal anonymous walks,” in Proc. Int. Conf. Learn. Represent. (ICLR), May 2021, pp. 1–21

    2021

  26. [26]

    Signed graph convolutional networks,

    T. Derr, Y . Ma, and J. Tang, “Signed graph convolutional networks,” in Proc. IEEE Int. Conf. Data Min (ICDM), Nov. 2018, pp. 929–934

    2018

  27. [27]

    Huang, H

    J. Huang, H. Shen, L. Hou, and X. Cheng,Signed Graph Attention Networks. Springer International Publishing, 2019, pp. 566–577

    2019

  28. [28]

    M. R. Islam, B. Aditya Prakash, and N. Ramakrishnan,SIGNet: Scalable Embeddings for Signed Networks. Springer International Publishing, 2018, pp. 157–169

    2018

  29. [29]

    Signed network embedding in social media,

    S. Wang, J. Tang, C. Aggarwal, Y . Chang, and H. Liu, “Signed network embedding in social media,” inProc. SIAM Int. Conf. Data Min. (SDM), 2017, pp. 327–335

    2017

  30. [30]

    SDGNN: Learning node representation for signed directed networks,

    J. Huang, H. Shen, L. Hou, and X. Cheng, “SDGNN: Learning node representation for signed directed networks,” inProc. AAAI Conf. Artif. Intell., May 2021, pp. 196–203

    2021

  31. [31]

    DynamiSE: Dynamic signed network embedding for link prediction,

    H. Sun, P. Tian, Y . Xiong, Y . Zhang, Y . Xiang, X. Jia, and H. Wang, “DynamiSE: Dynamic signed network embedding for link prediction,” Mach. Learn., vol. 113, no. 7, pp. 4037–4053, Jan. 2024

    2024

  32. [32]

    PolarDSN: An inductive approach to learning the evolution of network polarization in dynamic signed networks,

    M.-J. Kim, Y .-C. Lee, and S.-W. Kim, “PolarDSN: An inductive approach to learning the evolution of network polarization in dynamic signed networks,” inProc. ACM Int. Conf. Infor. Knowl. Mgmt. (CIKM), Oct. 2024, pp. 1099–1109

    2024

  33. [33]

    Representation learning in continuous-time dynamic signed networks,

    K. Sharma, M. Raghavendra, Y .-C. Lee, A. K. M, and S. Kumar, “Representation learning in continuous-time dynamic signed networks,” inProc. ACM Int. Conf. Infor. Knowl. Mgmt. (CIKM), Oct. 2023, pp. 2229–2238

    2023

  34. [34]

    Semi-Supervised Classification with Graph Convolutional Networks

    T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,”CoRR, vol. abs/1609.02907, 2016. [Online]. Available: http://arxiv.org/abs/1609.02907

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  35. [35]

    Graph Attention Networks

    P. Veli ˇckovi´c, G. Cucurull, A. Casanova, A. Romero, P. Li `o, and Y . Bengio, “Graph attention networks,” 2017. [Online]. Available: https://arxiv.org/abs/1710.10903v3

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  36. [36]

    Inductive representation learning on large graphs,

    W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” inProc. ACM Adv. Neural Inf. Process. Syst. (NeurIPS), Dec. 2017, pp. 1025–1035

    2017

  37. [37]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” inProc. ACM Adv. Neural Inf. Process. Syst. (NeurIPS), Dec. 2017, pp. 6000–6010

    2017

  38. [38]

    Neural Machine Translation by Jointly Learning to Align and Translate

    D. Bahdanau, K. Cho, and Y . Bengio, “Neural machine translation by jointly learning to align and translate,” 2014. [Online]. Available: https://arxiv.org/abs/1409.0473v7

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  39. [39]

    Collective dynamics of ‘small-world’ networks,

    D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,”Nature, vol. 393, no. 6684, pp. 440–442, Jun. 1998

    1998

  40. [40]

    Constructing a Watts- Strogatz network from a small-world network with symmetric degree distribution,

    M. B. C. Menezes, S. Kim, and R. Huang, “Constructing a Watts- Strogatz network from a small-world network with symmetric degree distribution,”PLOS ONE, vol. 12, no. 6, p. e0179120, Jun. 2017

    2017

  41. [41]

    Optimizing ma- chine learning for network inference through comparative analysis of model performance in synthetic and real-world networks,

    R. Khan, S. Khan, B. Pari, and K. Puszynski, “Optimizing ma- chine learning for network inference through comparative analysis of model performance in synthetic and real-world networks,”Sci. Rep., vol. 15, no. 1, Jul. 2025

    2025

  42. [42]

    Emergence of scaling in random net- works,

    A.-L. Barab ´asi and R. Albert, “Emergence of scaling in random net- works,”Science, vol. 286, no. 5439, pp. 509–512, Oct. 1999

    1999

  43. [43]

    Hossain, K

    T. Hossain, K. M. Saifuddin, M. I. K. Islam, F. Tanvir, and E. Akbas, Tackling Oversmoothing in GNN via Graph Sparsification. Springer Nature Switzerland, 2024, pp. 161–179

    2024

  44. [44]

    Historical Context Integration Module,

    INCODER, “Historical Context Integration Module,” gitHub repository, accessed May 2026. [Online]. Available: https: //github.com/incoder-mru/Historical-Context-Integration-Module

    2026

  45. [45]

    An introduction to ROC analysis,

    T. Fawcett, “An introduction to ROC analysis,”Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, Jun. 2006

    2006

  46. [46]

    Van Rijsbergen,Information Retrieval

    C. Van Rijsbergen,Information Retrieval. Butterworths, 1979. [Online]. Available: https://books.google.ca/books?id=t-pTAAAAMAAJ

    1979

  47. [47]

    Manning, P

    C. Manning, P. Raghavan, and H. Sch ¨utze,Introduction to Information Retrieval. Cambridge University Press, 2008

    2008

  48. [48]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,”

  49. [49]

    Decoupled Weight Decay Regularization

    [Online]. Available: https://arxiv.org/abs/1711.05101v3

    work page internal anchor Pith review Pith/arXiv arXiv