Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

Adipto Raihan Akib; Azmine Toushik Wasi; Mahathir Mohammad Bappy; MD Shafikul Islam

REVIEW 5 major objections 5 minor 22 references

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read The paper claims that modeling a supply chain as a graph—products as nodes linked by shared plants, groups, and storage—makes GNNs beat statistical and deep-learning baselines by 10–30% in regression and classification and 15–40% in…

desk verdict The SCG dataset is a real contribution, but the benchmark paper's headline superiority claim is undermined by likely label leakage in the relation detection/classification tasks. read the letter →

arxiv 2411.08550 v2 pith:WEST5I6T submitted 2024-11-13 cs.LG cs.CEstat.ML

Azmine Toushik Wasi , MD Shafikul Islam , Adipto Raihan Akib , Mahathir Mohammad Bappy This is my paper · ORCID

classification cs.LGcs.CEstat.ML

keywords GraphNeuralNetworksSupplyChainAnalyticsProductionPlanningDemandForecastingAnomalyDetectionHeterogeneousGraphsBenchmarkDatasetMachineLearning

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 paper argues that supply chains are naturally graph-structured and that graph neural networks (GNNs) are the right tool for supply chain analytics. The core claim is that connecting products as nodes—linked when they share a plant, product group, subgroup, or storage location—lets GNNs outperform traditional statistical and deep-learning baselines across six tasks: demand prediction, production forecasting, product classification, product relation classification and detection, and anomaly detection. On a new real-world benchmark dataset collected from a large FMCG company in Bangladesh, the reported gains are 10–30% in regression and classification and 15–40% in anomaly detection, with heterogeneous graphs (treating products, plants, and storage locations as distinct node types) generally performing best. The paper positions the dataset itself as a contribution, giving the research community a standard testbed for graph-based supply chain modeling.

What carries the argument

The central object is the supply-chain-as-graph formulation. Products are nodes; an edge connects two nodes when they share a plant, product group, subgroup, or storage location; and each node carries temporal features recording production, sales orders, deliveries, and factory issues in both units and metric tons. Homogeneous graphs use one node type, while heterogeneous graphs treat products, plants, and storage locations as distinct node types with typed edges. On these graphs, the paper applies established GNN architectures—convolutional, attentional, message-passing, temporal, and heterogeneous—which update each product's representation by aggregating features from its neighbors. The mechanism that carries the argument is message passing along these dependency edges: it lets a product's forecast or classification be influenced by the demand and production history of other products that share capacity, raw materials, or demand patterns.

What would settle it

Re-run the six benchmarks with the graph randomly rewired or with edges restricted to a single relation type, keeping node features unchanged; if GNN accuracy on product classification collapses toward the non-graph baselines whenever product-group edges are removed, the reported advantage is an artifact of how the graph encodes the labels.

Watch

Extended reading notes

Core claim

The paper's central discovery, on its own terms, is that modeling a supply chain as a graph transforms the performance profile of supply chain analytics. Treating each product as a node with temporal features (production, sales orders, deliveries to distributors, factory issues) and drawing edges between products that share a production plant, product group, subgroup, or storage location allows message-passing models to share information across related products. Benchmarking on the SCG dataset, every GNN-based model—convolutional, attentional, temporal, and heterogeneous variants—consistently beats the non-graph statistical and deep-learning alternatives on all six tasks. The authors report that graph models improve on baselines by roughly 10–30 percentage points in regression and classification and 15–40 percentage points in anomaly detection, measured on task-specific metrics, and that heterogeneous graph formulations usually do better than homogeneous ones. The discovery is not a new theoretical result but an empirical finding: the relational inductive bias of a graph matches the structure of supply chain data, and a public benchmark now makes that claim testable.

Load-bearing premise

The superiority claim rests on the single graph construction used throughout—products linked by shared plant, product group, subgroup, or storage location—being both informative for every one of the six tasks and free of leakage of the prediction target.

Editorial extensions

If this is right

Demand and production forecasting can be run jointly across all products of a company at once, with each product's prediction informed by the history of related products, rather than modeling each product independently.
Richer graph structure helps: heterogeneous graphs with product, plant, and storage node types consistently match or beat homogeneous graphs, so collecting typed relational metadata is worth the effort.
Anomaly detection in supply chain time series—stock-outs, demand spikes, disruptions—is where graph models show the largest relative advantage (15–40%), making them a candidate for early-warning systems.
The public benchmark dataset gives later researchers a standard testbed, so future GNN-versus-baseline comparisons in supply chain analytics become directly reproducible across six tasks.

Reading between the lines

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

The reported gains may partly reflect the edge construction rather than graph learning per se: because edges are defined in part by product group and subgroup, a model that predicts a product's group can read the answer off its neighbors. A fair test would ablate edges by relation type and check whether gains persist when group/subgroup edges are removed.
The dataset covers one company over eight months (January–August 2023); whether the 10–40% margins survive in other industries, longer horizons, or companies with different product portfolios is an open empirical question.
A natural next experiment is comparing GNNs against non-graph models that are given the same relational information explicitly (for example, neighbor-averaged features or one-hot group membership), which would separate the value of the graph representation from the value of the extra features.

Share X Bluesky LinkedIn Reddit HN

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

The SCG dataset is the real contribution here: a public, multi-perspective graph benchmark for FMCG supply chain planning, with six tasks, temporal node features, and an exploratory analysis. That is genuinely useful for a field with few public datasets, and the conceptual mapping of supply chains to graphs is adequate. The forecasting and anomaly detection experiments are suggestive: GNNs (TGCN, DCRNN, GDN) do beat LSTM/ARMA/PCA, though by moderate margins and without error bars or statistical tests.

The soft spots are serious, and one is load-bearing. The product relation detection and classification tasks (§7.1.4) look like label leakage. Edges are defined by shared product group, subgroup, plant, or storage location; the task is to predict presence/absence of those same edges; and Section 7 says plant connections are used as edges without describing any masking. If the adjacency matrix containing the positive labels is passed as input, a GNN can read the label directly from the graph, while non-graph baselines cannot. The 88–92% vs 66–78% gaps in those tasks are exactly the largest wins in Table 2 and drive the aggregate '10–30% in classification and detection' claim. Until those tasks are re-run under a proper link-prediction protocol (or the leakage is convincingly ruled out), the headline claim is not supported. This is not a minor weakness; it cuts the strongest advertised result.

Other issues are smaller but real. There are no error bars or hyperparameter details beyond 'default settings,' which makes it hard to tell whether GNN advantages are robust or artifacts of tuning. The license is inconsistent (Abstract/Data Availability say CC BY 4.0 at Zenodo; Section 5.1 says LGPL-2.1 on GitHub). The text misquotes one table entry (Section 7.2.4 cites 92.12% for homogeneous GCN, which is actually the heterogeneous detection number). Writing needs editing throughout, but that is cosmetic.

Who is this for? Researchers looking for a public dataset to test GNNs on supply chain planning. They will get value from the data and task definitions, but they should not trust the benchmark superiority claims as-is. The paper deserves a serious referee; the dataset is novel and the forecasting experiments are worth evaluating properly. I would send it to review but require major revision: re-run relation tasks with genuine edge masking, add error bars and a hyperparameter budget, fix the license inconsistency, and release code. If the authors do that, the paper could be a solid benchmark reference. As it stands, I would not cite it in the next year.

Referee Report

5 major / 5 minor

Summary. The paper proposes a graph-based formulation of supply chain planning, introduces a new benchmark dataset (SCG) collected from a Bangladeshi FMCG company, and benchmarks graph neural network (GNN) models against statistical, boosting, and deep-learning baselines on six tasks: demand forecasting, production forecasting, product classification, product relation classification, product relation detection, and anomaly detection. The experiments are run on both homogeneous and heterogeneous graph constructions. The central claim, stated in the abstract and conclusion, is that GNN-based models consistently outperform non-graph baselines by 10-30% in regression and classification/detection tasks and by 15-40% in anomaly detection. The paper also provides conceptual discussion of GNNs for supply chains, task formulations, dataset statistics, and a discussion of limitations and future work.

Significance. The dataset and conceptual framework are potentially useful to the supply-chain machine-learning community: the SCG dataset is publicly available with a DOI, the paper covers six distinct tasks, includes both homogeneous and heterogeneous graph formulations, and compares several GNN variants with classical baselines. The honest Limitations paragraph in Section 8 correctly notes the short temporal span of the data. However, the paper's central empirical claim is currently not supported because of a likely label-leakage problem in the relation tasks and the absence of statistical validation. If the authors re-run the experiments under a proper link-prediction protocol and report variance estimates, the benchmark could become a valuable resource. As written, the quantitative superiority claims in the abstract and conclusion are overstated relative to the evidence in Table 2.

major comments (5)

[§4.1, §7.1.4, Table 2(g)–(j)]
[§7.2, §7.1.1, §7.1.2]
[Abstract and §9 vs. Table 2(k)–(l)]
[§4.1, §7]
[§5.1, §8]

minor comments (5)

[§7.2.4]
[Table 2(g)–(j)]
[§5.1 and Data Availability]
[§7.2.3]
[Throughout]

Circularity Check

1 steps flagged · score 6.0 of 10

Product relation detection/classification predicts the same edge set that is supplied as the GNN adjacency input, so part of the claimed outperformance is forced by construction.

self definitional [Section 4.1 and Section 7.1.4 (also Table 2(g)–2(j))]
"Following discussions on supply chain as graph in Section 4 and general GNN formulations in Section 3.1, we can formulate a supply chain graph G = (V, E) by considering each product as a node u ∈ V, and the connections between products—such as being manufactured in the same factory or belonging to the same product group or subgroup as edges (u, v) ∈ E. ... Product relation detection entailed a binary classification task focused on discerning the presence or absence of edges, while product relation classification involved a multi-class classification task."

The GNN input includes the adjacency matrix A, and the task labels are the presence or absence of edges, i.e., the entries of A. If test edges are not masked from the adjacency, then a GCN/GAT can read the answer directly from its input graph, while the non-graph baselines (logistic regression, XGB, KNN, ANN) only receive node features and never see the adjacency. The paper does not describe any link-prediction masking protocol or negative-edge sampling for this task, so the large reported gaps in Table 2(g)–(j) (e.g., 88–92% vs. 66–78% accuracy) are explained by the label being in the input rather than by learned graph generalization.

full rationale

The paper is an empirical benchmark, not a derivation from fitted parameters, so most of its tasks are not circular. Demand/production forecasting uses temporal node features (production, sales orders, delivery, factory issues) with a train/test split, product classification is described with plant-similarity edges rather than product-group edges, and anomaly detection labels are temporal deviations, not graph edges. These tasks have independent content and the GNN gains there are not definitionally forced. The significant circularity is concentrated in product relation detection and relation classification: the graph construction in Section 4.1 defines edges by the very relations (same product group, subgroup, plant, storage location) that Section 7.1.4 asks the model to detect or classify, and Section 3.1 makes the adjacency matrix an input to the GNN. Since no masking of test edges is reported, the positive labels are in the input adjacency, so the GNN's advantage over nongraph baselines in those tasks is partly tautological. This does not invalidate the forecasting, product-classification, or anomaly-detection results, but it means the blanket 10–30% 'classification and detection' claim should not be taken at face value until the relation tasks are rerun under a proper link-prediction protocol. Self-citations in the introduction are not load-bearing, and no uniqueness theorem or ansatz is smuggled in via citation. Overall score 6: one or more predictions reduce by construction, while other central claims remain independent.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new theoretical entities. The central 'modeling choice' is the graph construction, which is a domain assumption rather than an invented entity.

free parameters (1)

Model hyperparameters (e.g., learning rate, hidden units, attention heads, number of layers)
Section 7.1 says default hyperparameters were used but does not list them; benchmark outcomes depend on these undisclosed choices, so they act as implicit free parameters.

assumptions (3)

domain assumption The supply chain can be faithfully represented as a graph where nodes are products and edges encode shared plant, product group, storage, or subgroup relations.
Defined in Section 4.1 and used throughout the experiments; no validation that this graph structure captures the true causal dependencies.
domain assumption The temporal node features (production, sales order, delivery, factory issue) are sufficient for the six benchmark tasks.
Section 5.2 lists the features; the paper does not test sensitivity to feature set.
domain assumption GNN architectures designed for generic graphs transfer without modification to supply chain planning tasks.
Section 7 uses off-the-shelf models from PyTorch Geometric and PyTorch Geometric Temporal; no supply-chain-specific adaptation is investigated.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks." pith.science (2026). https://pith.science/paper/WEST5I6T

@misc{pith2026241108550,
  author       = {Pith},
  title        = {Pith review of: Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WEST5I6T}},
  note         = {Machine review of arXiv:2411.08550}
}

read the original abstract

Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

Figures

Figures reproduced from arXiv: 2411.08550 by the authors.

**Figure 1.** Supply Chain as a Graph of Interconnected Company, Products, Distributors and Customers. data, with relationships and dependencies between entities requiring sophisticated models to capture (Sadeghiamirshahidi et al. 2014). The potential benefits of using computational methods to solve supply chain problems include improved coordination, efficient logistics, and effective supply chain solutions (Chaovalitwongse and… view at source ↗

**Figure 2.** Supply Chain Problem Formulation in Homogeneous Graph. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Heterogeneous Graph Example using SCG dataset. Products, plants, and storage locations are nodes and their relations are edges. (a) Here, the nodes are sub-group products, and plants are edges. Colors denote different types of nodes and edges. (b) Here, nodes plant-products, and storage locations are edges. Colors denote different types of nodes and edges [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Homogeneous Graph Examples using SCG dataset. 11 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Dataset Statistics [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Data Samples. • Delivery to Distributors denotes dispatched products aligning with orders, impacting company revenue significantly. For example, delivery data might show that 450 units or 9 metric tons have been sent to distributors. • Factory Issue covers total produ…

**Figure 7.** Figure 7: Temporal correlations. 5.3.2. Analyzing Temporal Trends [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 15 canonical work pages

[4]

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

“Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.”CoRRabs/2104.13478. Cai, Lei, and Shuiwang Ji

arXiv
[8]

Demand forecasting using ensemble learning for effective schedul- ing of logistic orders

“Demand forecasting using ensemble learning for effective schedul- ing of logistic orders.” InAdvances in Artificial Intelligence, Software and Systems Engineering: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, July 25-29, 2021, USA, 313–321....

work page 2021
[9]

Geometric deep learning on graphs and manifolds using mixture model cnns

“Geometric deep learning on graphs and manifolds using mixture model cnns.” InCVPR, . Mrad,Mariem,andYounesBoujelbene.2019.“AnImprovedNeuralApproachesforForecasting Demand in Supply Chain Management.”International Journal of Computer Applications975:

work page 2019
[11]

Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets

“Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets.”IEEE Access8: 154540–154559. https://doi.org/10.1109/ACCESS.2020.3015910. Pirhooshyaran, Mohammad, and Lawrence V Snyder

arXiv 2020
[12]

Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

“Simultaneous decision making for stochastic multi-echelon inventory optimization with deep neural networks as decision makers.”arXiv preprint arXiv:2006.05608. Polo-Triana,Sonia,JuanCamiloGutierrez,andJuanLeon-Becerra.2024.“Integrationofmachine learning in the supply chain for decision making: A systematic literature review.”Journal of Industrial Enginee...

work page Pith review arXiv 2006
[13]

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

“PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models.” InProceedings of the 30th ACM International Conference on Information and Knowledge Management, 4564–4573. Sadeghiamirshahidi,Narjes,JafarAfshar,AliRezaFirouzi,andSAHSHassan.2014.“Improving the efficiency of manufacturing supply chain using system dynamic s...

work page 2014
[14]

Network In Graph Neural Network

“Network in graph neural network.”arXiv preprint arXiv:2111.11638. Sudrich, Simon, Julio Borges, and Michael Beigl

work page Pith review arXiv
[18]

HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation

“HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation.”In Proceedingsofthe1stWorkshop on Knowledge Graphs and Large Language Models (KaLLM 2024), Bangkok, Thailand, August, 56–62. Association for Computational Linguistics. Wasi, Azmine Toushik, Taki Hasan Rafi, Raima Islam, and Dong-Kyu Chae. 2024a. “Bang...

work page 2024

Show all 22 references

[20]

Gaan: 26 Gatedattentionnetworksforlearningonlargeandspatiotemporalgraphs

“Gaan: 26 Gatedattentionnetworksforlearningonlargeandspatiotemporalgraphs.” arXiv:1803.07294 . Zhang,Muhan,andYixinChen.2017.“Weisfeiler-LehmanNeuralMachineforLinkPrediction.” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ...

2017 arXiv
[21]

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

“Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method.”ArXiv abs/1811.05320. Zhong, Weida, Qiuling Suo, Xiaowei Jia, Aidong Zhang, and Lu Su

arXiv
[22]

Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values

“Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values.” In2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 707–717. Zhou, Zhanting, Kejun Bi, Yuyanzhen Zhong, Chao Tang, Dongfen Li, Shi Ying, and R...

2023
[2005]

Supply- chain networks: a complex adaptive systems perspective

“Supply- chain networks: a complex adaptive systems perspective.”International Journal of Production Research43 (20): 4235–4265. Tang, Zheng,andYuemengGe. 2022.“CNNmodeloptimizationandintelligent balancemodel for material demand forecast.”International Journal of System Assura...

2022
[2010]

Assessing the vulnerability of supply chains usinggraphtheory

“Assessing the vulnerability of supply chains usinggraphtheory.” InternationalJournalofProductionEconomics 126(1):121–129.Improving Disaster Supply Chain Management – Key supply chain factors for humanitarian relief, https://doi.org/https://doi.org/10.1016/j.ijpe.2009.10.007. ...

doi
[2014]

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

“Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.” . Dai,Enyan,andJieChen.2022.“Graph-AugmentedNormalizingFlowsforAnomalyDetection of Multiple Time Series.” InInternational Conference on Learning Representations, . Defferrard, Michaël, Xavier Bress...

2022
[2017]

Neural message passing for quantum chemistry

“Neural message passing for quantum chemistry.”arXiv:1704.01212. Hamilton, William L. 2020.Graph Representation Learning. Springer International Publishing. Higuchi,Toru,andMarvinDTroutt.2004.“Dynamicsimulationofthesupplychainforashort life cycle product—Lessons from the Tamag...

2020 arXiv
[2018]

Relational inductive biases, deep learning, and graph networks

“Relational inductive biases, deep learning, and graph networks.”arXiv:1806.01261. Bousqaoui, Halima, Ilham Slimani, and Said Achchab

arXiv
[2019]

Demand forecastingusingrandomforestandartificialneuralnetworkforsupplychainmanagement

“Demand forecastingusingrandomforestandartificialneuralnetworkforsupplychainmanagement.” In Computational Collective Intelligence: 11th International Conference, ICCCI 2019, Hendaye, France, September 4–6, 2019, Proceedings, Part I 11, 328–339. Springer. Veličković, Petar, Gui...

2019
[2020]

Data analytics in the supplychainmanagement:Reviewofmachinelearningapplicationsindemandforecasting

“Data analytics in the supplychainmanagement:Reviewofmachinelearningapplicationsindemandforecasting.” Operations and Supply Chain Management: An International Journal14 (1): 1–13. Acemoglu,Daron,andAlirezaTahbaz-Salehi.2020. Firms,Failures,andFluctuations:TheMacroe- conomics o...

2020
[2021]

Data considerations in graph representation learning for supply chain networks

“Data considerations in graph representation learning for supply chain networks.”arXiv preprint arXiv:2107.10609. Battaglia, Peter W, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zam- baldi, Mateusz Malinowski, Andrea Tacchetti, et al

arXiv
[2023]

Industry classification based on supply chain network information using Graph Neural Networks

“Industry classification based on supply chain network information using Graph Neural Networks.”Applied Soft Computing 132: 109849. Wu,Felix,AmauriSouza,TianyiZhang,ChristopherFifty,TaoYu,andKilianWeinberger.2019. “Simplifying graph convolutional networks.” InICML, . Xiang, Sh...

2019
[2024]

Graph Artificial Intelligence in Medicine

“Graph Artificial Intelligence in Medicine.” Annual Review of Biomedical Data Science https://doi.org/10.1146/annurev-biodatasci-110723-024625. Kipf, Thomas N., and Max Welling

doi
[8887]

Papajorgji, and Panos M

Mucherino, Antonio, Petraq J. Papajorgji, and Panos M. Pardalos. 2009.k-Nearest Neighbor Classification, 83–106. New York, NY: Springer New York. Nitsche, Anna-Maria, Christian-Andreas Schumann, Bogdan Franczyk, and Kevin Reuther

2009

Pith tools

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

[1] [4]

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

“Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.”CoRRabs/2104.13478. Cai, Lei, and Shuiwang Ji

arXiv

[2] [8]

Demand forecasting using ensemble learning for effective schedul- ing of logistic orders

“Demand forecasting using ensemble learning for effective schedul- ing of logistic orders.” InAdvances in Artificial Intelligence, Software and Systems Engineering: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, July 25-29, 2021, USA, 313–321....

work page 2021

[3] [9]

Geometric deep learning on graphs and manifolds using mixture model cnns

“Geometric deep learning on graphs and manifolds using mixture model cnns.” InCVPR, . Mrad,Mariem,andYounesBoujelbene.2019.“AnImprovedNeuralApproachesforForecasting Demand in Supply Chain Management.”International Journal of Computer Applications975:

work page 2019

[4] [11]

Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets

“Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets.”IEEE Access8: 154540–154559. https://doi.org/10.1109/ACCESS.2020.3015910. Pirhooshyaran, Mohammad, and Lawrence V Snyder

arXiv 2020

[5] [12]

Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

“Simultaneous decision making for stochastic multi-echelon inventory optimization with deep neural networks as decision makers.”arXiv preprint arXiv:2006.05608. Polo-Triana,Sonia,JuanCamiloGutierrez,andJuanLeon-Becerra.2024.“Integrationofmachine learning in the supply chain for decision making: A systematic literature review.”Journal of Industrial Enginee...

work page Pith review arXiv 2006

[6] [13]

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

“PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models.” InProceedings of the 30th ACM International Conference on Information and Knowledge Management, 4564–4573. Sadeghiamirshahidi,Narjes,JafarAfshar,AliRezaFirouzi,andSAHSHassan.2014.“Improving the efficiency of manufacturing supply chain using system dynamic s...

work page 2014

[7] [14]

Network In Graph Neural Network

“Network in graph neural network.”arXiv preprint arXiv:2111.11638. Sudrich, Simon, Julio Borges, and Michael Beigl

work page Pith review arXiv

[8] [18]

HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation

“HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation.”In Proceedingsofthe1stWorkshop on Knowledge Graphs and Large Language Models (KaLLM 2024), Bangkok, Thailand, August, 56–62. Association for Computational Linguistics. Wasi, Azmine Toushik, Taki Hasan Rafi, Raima Islam, and Dong-Kyu Chae. 2024a. “Bang...

work page 2024