Paper Citation Record · LEDGER

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2411.14035.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.14035 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:26:20.438770Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T17:44:19.088737Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation c0e6712d-c3af-4f39-a951-cdd3f6e1a6cf · outbound

This paper cites Heterogeneous network representation learning: A unified framework with survey and benchmark,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Heterogeneous network representation learning: A unified framework with survey and benchmark,

Reference 1

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Observation f70630a9-fc0b-4440-8780-9c61befacde9 · outbound

This paper cites Online user representation learning across heterogeneous social networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Online user representation learning across heterogeneous social networks,

Reference 2

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Observation 21c0962f-e205-45ec-8c8c-27ae7bb7fa3e · outbound

This paper cites Oag: Linking entities across large-scale heterogeneous knowledge graphs,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Oag: Linking entities across large-scale heterogeneous knowledge graphs,

Reference 3

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Observation 4e5066e2-6741-497a-86d1-69759103b86a · outbound

This paper cites Heterogeneous informa- tion network embedding for recommendation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Heterogeneous informa- tion network embedding for recommendation,

Reference 4

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Observation eeae88d0-97fa-47db-be28-d4385aca7274 · outbound

This paper cites Connecting embeddings based on multiplex relational graph attention networks for knowledge graph entity typing,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Connecting embeddings based on multiplex relational graph attention networks for knowledge graph entity typing,

Reference 5

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Observation ead0b8eb-5e6d-41d0-ad39-057d11b8017e · outbound

This paper cites Single-cell biological network inference using a heterogeneous graph transformer,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Single-cell biological network inference using a heterogeneous graph transformer,

Reference 6

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Observation e2b29ec4-6691-42b9-8d14-705c4d2ee9f9 · outbound

This paper cites Modeling relational data with graph convolutional networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Modeling relational data with graph convolutional networks,

Reference 7

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Observation f139d2dc-8b21-4935-ad33-4147440c85f8 · outbound

This paper cites Interpretable and efficient heterogeneous graph convolutional network,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Interpretable and efficient heterogeneous graph convolutional network,

Reference 8

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Observation e6edef59-5bec-4319-af66-f16316deaded · outbound

This paper cites Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,

Reference 9

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Observation 79ae7407-3443-40fe-a7b4-d1c44953e48f · outbound

This paper cites Hgamlp: Heterogeneous graph attention mlp with de-redundancy mech- anism,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Hgamlp: Heterogeneous graph attention mlp with de-redundancy mech- anism,

Reference 10

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Observation 537898ef-dd6e-4fd7-840a-20361f134fb8 · outbound

This paper cites Heterogeneous graph attention network,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Heterogeneous graph attention network,

Reference 11

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Observation 3bbb3a17-68cf-44c5-9f1e-2c395d31ba17 · outbound

This paper cites Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,

Reference 12

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Observation dc5c74b2-d64a-4107-858d-2a49c82d4921 · outbound

This paper cites Heterogeneous graph propagation network,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Heterogeneous graph propagation network,

Reference 13

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Observation f57755a3-8331-4c18-bf22-79286ff4f0a5 · outbound

This paper cites Reliable node sim- ilarity matrix guided contrastive graph clustering,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Reliable node sim- ilarity matrix guided contrastive graph clustering,

Reference 14

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Observation 8965ae89-fdb2-41d0-ae14-6b12323230c2 · outbound

This paper cites Paths2pair: Meta-path based link prediction in billion-scale commercial heterogeneous graphs,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Paths2pair: Meta-path based link prediction in billion-scale commercial heterogeneous graphs,

Reference 15

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Observation 43772f09-8037-446f-8412-7b406102fb4d · outbound

This paper cites Igb: Addressing the gaps in labeling, features, heterogeneity, and size of public graph datasets for deep learning research,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Igb: Addressing the gaps in labeling, features, heterogeneity, and size of public graph datasets for deep learning research,

Reference 16

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Observation 5a8ee6a3-f0c8-445c-9885-69ebb1d278b3 · outbound

This paper cites Graph-less neural networks: Teaching old MLPs new tricks via distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Graph-less neural networks: Teaching old MLPs new tricks via distillation,

Reference 17

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Observation 6cf0b43a-d5a3-4af6-989e-16942ceed15d · outbound

This paper cites Learning MLPs on graphs: A unified view of effectiveness, robustness, and efficiency,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Learning MLPs on graphs: A unified view of effectiveness, robustness, and efficiency,

Reference 18

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Observation b893552c-5568-432b-8db4-94e19fad6b1d · outbound

This paper cites Quantifying the knowledge in gnns for reliable distillation into mlps,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Quantifying the knowledge in gnns for reliable distillation into mlps,

Reference 19

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This paper cites VQGraph: Rethinking graph representation space for bridging GNNs and MLPs,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference VQGraph: Rethinking graph representation space for bridging GNNs and MLPs,

Reference 20

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Observation d5af9cbe-02a0-4b29-95fe-b0c9893b7a82 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Distilling the Knowledge in a Neural Network

Reference 21

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This paper cites Semi-supervised classification with graph convolutional networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Semi-supervised classification with graph convolutional networks,

Reference 22

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This paper cites Graph attention networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Graph attention networks,

Reference 23

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This paper cites Double wins: Boosting accuracy and efficiency of graph neural networks by reliable knowledge distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Double wins: Boosting accuracy and efficiency of graph neural networks by reliable knowledge distillation,

Reference 24

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Observation 9566b457-a69d-42ca-baab-cd05a0d525ca · outbound

This paper cites Extracting low-/high- frequency knowledge from graph neural networks and injecting it into mlps: An effective gnn-to-mlp distillation framework,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Extracting low-/high- frequency knowledge from graph neural networks and injecting it into mlps: An effective gnn-to-mlp distillation framework,

Reference 25

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Observation f2a61737-bd36-40ff-aff0-82a23b31d87d · outbound

This paper cites Linkless link prediction via relational distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Linkless link prediction via relational distillation,

Reference 26

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This paper cites Mugsi: Distilling gnns with multi-granularity structural information for graph classification,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Mugsi: Distilling gnns with multi-granularity structural information for graph classification,

Reference 27

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Observation 7c6c45c7-8e4f-49f8-8bbb-ee565a310099 · outbound

This paper cites LightHGNN: Distilling hy- pergraph neural networks into MLPs for 100x faster inference,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference LightHGNN: Distilling hy- pergraph neural networks into MLPs for 100x faster inference,

Reference 28

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Observation daef2616-7232-418c-ac88-cdf615c686df · outbound

This paper cites In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection frame- work for semi-supervised learning,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection frame- work for semi-supervised learning,

Reference 29

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This paper cites Re- liable data distillation on graph convolutional network,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Re- liable data distillation on graph convolutional network,

Reference 30

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Observation a379a6eb-b507-44a3-9dfa-1d1a434a7985 · outbound

This paper cites Deep insights into noisy pseudo labeling on graph data,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Deep insights into noisy pseudo labeling on graph data,

Reference 31

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Observation d0071e1c-4c9f-40f9-a755-32e6af6b90da · outbound

This paper cites Self-supervised heterogeneous graph neural network with co-contrastive learning,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Self-supervised heterogeneous graph neural network with co-contrastive learning,

Reference 32

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Observation 7b030171-5485-4b18-9fc8-bb3c3855b7d4 · outbound

This paper cites OGB- LSC: A large-scale challenge for machine learning on graphs,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference OGB- LSC: A large-scale challenge for machine learning on graphs,

Reference 33

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Observation 639f946e-ed13-4eb9-8407-0f0ead4fdcb0 · outbound

This paper cites On graph neural networks versus graph-augmented mlps,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference On graph neural networks versus graph-augmented mlps,

Reference 34

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Observation 84bdc245-ce1e-4324-8a21-455e3eb24416 · outbound

This paper cites Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator

Reference 35

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Observation 968b7a46-58bf-43c0-baaf-5f3e0af71ba5 · outbound

This paper cites Joint embedding of struc- ture and features via graph convolutional networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Joint embedding of struc- ture and features via graph convolutional networks,

Reference 36

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Observation e1b48617-aad2-4f86-8996-9d7c867a40a9 · outbound

This paper cites Multi-scale heterogeneous text-attributed graph datasets from diverse domains,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Multi-scale heterogeneous text-attributed graph datasets from diverse domains,

Reference 37

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Observation 52037565-e50c-4fa9-8394-469fea40cefb · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre- trained transformers,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Minilm: Deep self-attention distillation for task-agnostic compression of pre- trained transformers,

Reference 38

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Observation fb04f29e-32b5-481c-9bbe-faf7e77c93a6 · outbound

This paper cites Sentence-BERT: Sentence embeddings using Siamese BERT-networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Sentence-BERT: Sentence embeddings using Siamese BERT-networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:49.052925Z

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Observation 8f538768-a9f2-419b-bf09-9a783f4df6a0 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 40

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This paper cites Inductive representation learn- ing on large graphs,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Inductive representation learn- ing on large graphs,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:49.028468Z

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Observation d07aba24-415c-4356-939c-f8bd1ec54792 · outbound

This paper cites Relational Graph Attention Networks.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Relational Graph Attention Networks

Reference 42

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This paper cites Adam: A method for stochastic optimization,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Adam: A method for stochastic optimization,

Reference 43

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Observation 12ce9d6a-eb40-4280-814b-822e612b4fa6 · outbound

This paper cites Learning accurate, efficient, and interpretable mlps on multiplex graphs via node- wise multi-view ensemble distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Learning accurate, efficient, and interpretable mlps on multiplex graphs via node- wise multi-view ensemble distillation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:48.993012Z

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Observation 8c86fda8-2665-45a5-9828-9a5e29eeb67e · outbound

This paper cites Hire: Distilling high-order relational knowledge from heterogeneous graph neural networks,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Hire: Distilling high-order relational knowledge from heterogeneous graph neural networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:48.975117Z

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Observation 32d4680c-9181-48b0-a2be-1f2b2bede95c · outbound

This paper cites A teacher-free graph knowledge distillation framework with dual self-distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference A teacher-free graph knowledge distillation framework with dual self-distillation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:48.957490Z

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Observation b03299db-c554-49ed-84a0-5efca653815e · outbound

This paper cites Single teacher, multiple perspectives: Teacher knowledge augmentation for enhanced knowledge distillation,.

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference Single teacher, multiple perspectives: Teacher knowledge augmentation for enhanced knowledge distillation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:42:48.939929Z

Source-reported events for the cited work

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Pith citing papers

Observation 0e86f697-c2f5-4f88-8d96-04f715e08af1 · inbound

Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains cites this paper.

Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference

Reference 23

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Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation cites this paper.

Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference

Reference 12

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