# GCN-MPPR: Enhancing the Propagation of Message Passing Neural Networks via Motif-Based Personalized PageRank

arXiv: https://arxiv.org/abs/2602.07903 · HTML: https://mlsanity.com/paper/2602.07903/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 8 Feb 2026.

## Builds on (most cited first, 25 of 25)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [Higher-order organization of complex networks](https://mlsanity.com/paper/1612.08447/) (2016)
- [RAW-GNN: RAndom Walk Aggregation based Graph Neural Network](https://mlsanity.com/paper/2206.13953/) (2022)
- [Graph Neural Networks with Diverse Spectral Filtering](https://mlsanity.com/paper/2312.09041/) (2023)
- [Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts](https://mlsanity.com/paper/1411.4942/) (2014)
- [Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials](https://mlsanity.com/paper/2305.19872/) (2023)
- [Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory](https://mlsanity.com/paper/2402.15326/) (2024)
- [Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks](https://mlsanity.com/paper/2406.05482/) (2024)
- [Hyperdimensional Representation Learning for Node Classification and Link Prediction](https://mlsanity.com/paper/2402.17073/) (2024)
- [Hyperbolic Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2404.09456/) (2024)
- [GraSP: Simple yet Effective Graph Similarity Predictions](https://mlsanity.com/paper/2412.09968/) (2024)
- [Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks](https://mlsanity.com/paper/2502.14158/) (2025)
- [Ranking Users in Social Networks with Motif-based PageRank](https://mlsanity.com/paper/1912.11817/) (2019)
- [Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?](https://mlsanity.com/paper/2505.16736/) (2025)
- [The Oversmoothing Fallacy: A Misguided Narrative in GNN Research](https://mlsanity.com/paper/2506.04653/) (2025)
- [Graph Classification via Discriminative Edge Feature Learning](https://mlsanity.com/paper/2210.02060/) (2022)
- [Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing](https://mlsanity.com/paper/2407.15284/) (2024)
- [Effective Clustering for Large Multi-Relational Graphs](https://mlsanity.com/paper/2508.17388/) (2025)
- [Counting Cohesive Subgraphs with Hereditary Properties](https://mlsanity.com/paper/2405.04823/) (2024)
- [Counting Small Induced Subgraphs: Hardness via Fourier Analysis](https://mlsanity.com/paper/2407.07051/) (2024)

## Abstract (arXiv)

The algorithms based on message passing neural networks (MPNNs) on graphs have recently achieved great success for various graph applications. However, studies find that these methods always propagate the information to very limited neighborhoods with shallow depth, particularly due to over-smoothing. That means most of the existing MPNNs fail to be so `deep'. Although some previous work tended to handle this challenge via optimization- or structure-level remedies, the overall performance of GCNs still suffers from limited accuracy, poor stability, and unaffordable computational cost. Moreover, neglect of higher-order relationships during the propagation of MPNNs has further limited the performance of them. To overcome these challenges, a novel variant of PageRank named motif-based personalized PageRank (MPPR) is proposed to measure the influence of one node to another on the basis of considering higher-order motif relationships. Secondly, the MPPR is utilized to the message passing process of GCNs, thereby guiding the message passing process at a relatively `high' level. The experimental results show that the proposed method outperforms almost all of the baselines on accuracy, stability, and time consumption. Additionally, the proposed method can be considered as a component that can underpin almost all GCN tasks, with DGCRL being demonstrated in the experiment. The anonymous code repository is available at: https://anonymous.4open.science/r/GCN-MPPR-AFD6/.