# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 6 Conclusions  
This paper introduces HGPN, a novel framework for heterogeneous graph representation learning. Unlike existing HGNN methods that struggle with structural and informational noise and rely on human intervention, HGPN is designed to autonomously purify both types of heterogeneity without hand-engineered metapaths. HGPN employs type-specific subgraph decomposition to efficiently model heterogeneity and incorporates type-specific batch normalization and cluster-aware loss for feature purification. Additionally, the neighborhood smoothness-related dynamic residual connection improves the optimization of nodes with lower quality features. Experiments on various datasets and training settings demonstrate that HGPN consistently achieves superior performance. Further analysis confirms the effectiveness of HGPN's feature and structure purification methods. Future work will focus on exploring HGPN's causal interpretability and expanding the framework to temporal dynamic graphs for enhanced applicability in realistic scenarios.  
Conceptualization, S.S. and Z.Z.; methodology, S.S.; software, S.S. and S.L.; validation, S.S. and Z.Z.; formal analysis, S.S.; investigation, S.S.; resources, S.S. and D.Z.; data curation, S.S., P.D., Q.L. and X.L.; writing--original draft preparation, S.S.; writing--review and editing, S.S., D.Z., S.L., P.D., Q.L., X.L. and Z.Z.; visualization, S.S.; supervision, D.Z., S.L. and Z.Z.;  
Figure 3: Purification rate of each edge type on OGB-MAG under 2 training settings. “w” means “writes”, “c” means “cites”, “ht” means “has topic”, “aw” means “affiliated with”. “r-x” means the reverse type of edge type “x”. “Hop_i” denotes neighbours from i-th hop, and “Hop_i–L” denotes “Hop_i” with the training setting of “Label Input”.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Edge/Num**} & \multicolumn{3}{c}{**Original Setting**} & \multicolumn{2}{c}{**Label Input Setting**} \\ \cline{2-5}  & **Hop 1** & **Hop 2** & **Hop 1** & **Hop 2** \\ \hline w/7,145,660 & 69 & 0 & 1 & 0 \\ r-w/7,145,660 & 5710 & 698,753 & 0 & 698,192 \\ c/5,416,271 & 195,939 & 2058 & 0 & 0 \\ r-c/5,416,271 & 0 & 0 & 0 & 0 \\ ht/7,505,078 & 1283 & 7,338,815 & 0 & 205,404 \\ r-ht/7,505,078 & 7,505,078 & 0 & 0 & 599 \\ aw/1,043,998 & 833,143 & 1,042,902 & 31,500 & 17,070 \\ r-aw/1,043,998 & 1,043,907 & 1168 & 1,043,996 & 1,040,857 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistics of numbers of edges that HGPN learns to purify under 2 two training settings. “w” means “writes”, “c” means “cites”, “ht” means “has topic”, “aw” means “affiliated with”. “r-x” means the reverse type of edge type “x”. “Hop i’ denotes neighbours from \(i\)-th hop.  
project administration, S.S. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support this study are available online in [[https://ogb.stanford.edu](https://ogb.stanford.edu)]([https://ogb.stanford.edu](https://ogb.stanford.edu)) (accessed on 2 May 2020) and [[https://github.com/THUDM/HGB](https://github.com/THUDM/HGB)]([https://github.com/THUDM/HGB](https://github.com/THUDM/HGB)) (accessed on 2 May 2023).  
The authors declare that they do not have any conflict of interest. This research does not involve any human or animal participation.