Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a novel domain adaptation method on heterogeneous graphs by reconstructing the source node embeddings through target node embeddings. Theoretical proofs verify the correctness of proposed method. Experiments show that the proposed method achieves significant performance promotion compared with bare HGNN model. 
strengths_and_weaknesses: Strengths：

- This paper is good structured and quite informative. Nice writing.
- Figure 1 gives a simple and clear example, demonstrating how the HGNN is trained with respect to different node types. The combination of Figure 1 and Figure 2 justifies the motivation of this paper.
- Theoretical results in Section 4.3 is intuitive, easy to understand. Theorem 1 induces an elegant algorithm in Section 5.
- Extensive experiments validate the effectiveness (Table 1 & 2) and generality (Table 3) of the proposed method.Weaknesses: 
    
Weaknesses：
- The methods compared in Table 1 seem to be out-dated. Some recents works could be considered, i.e. Adversarial-Learned Loss for Domain Adaptation (AAAI2020).
questions: 1. Authors should more clearly state how the gradients are computed in Figure 2 (b)-(c), i.e. they are gradients under loss $\mathcal{L}_s$.
limitations: yes
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper studies the zero-shot node types in knowledge transfer and proposes a model to transfer knowledge from label-abundant node types to zero-labeled ones. The main contributions of this paper lie in the proposed practical scenario and problem definition, i.e., knowledge transfer of zero-shot node types. The transferability of node label knowledge is theoretically analyzed. A node type label knowledge transfer network is proposed.
strengths_and_weaknesses: Strengths:

- This paper studies a novel and practical problem, and the motivation is clearly stated.

- The proposed graph neural network is supported by theoretical analysis, although some of the analysis is based on a simple toy graph.

- Experimental results show that the proposed method outperforms the baseline methods.

Weaknesses:

- The title is misleading. There is a lot of transferable knowledge in the graph data, but this paper only discusses a specific setting of the node type labels.

- The baseline methods are from 2005–2018. Some new work needs to be discussed.

- As a work that proposes a new problem setting, Section 6.3 needs to provide more details of the experimental setting.
questions: This work proposes a new practical problem definition, which is beneficial to the development of the field. But I have a few confusing questions about the paper. Please point out if I misunderstood.

- In the problem definition, the authors say that only source type labels are available. Although the target labels are not used as training samples, the training graph data seems to contain the labels of the target type (i. target type related aggregation and ii. output embeddings of the nodes of the target type). Is this reasonable? This is my biggest concern.

- Does the proposed method still work on other graph tasks (such as link prediction) and on more complex graphs (such as knowledge graphs)? No need to add experiments, just give a brief statement about feasibility.

- If needed, you can respond to the above *Weaknesses* to help me make a better assessment.
limitations: N/A.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper notes that many HG datasets suffer from label imbalance between node types, and proposes a zero-shot transefer learning module to transfer knowledge from label-abundant node types to zero-labeled node types. This is a good work in my opinion.
strengths_and_weaknesses: - Strengths
  - This work notes a good research problem. The issue of label imbalance among different node types is common in existing HG datasets.
  - The proposed method KTN has a solid theoretical foundation, and its implementation is quite easy.
  - The experimental results show that KTN is very effective in improving the performance of existing HGNN models.
  - The provided source codes and datasets in the supplemental material facilitates the good reproducibility of this work.

- Weaknesses
  - In section 3.2, the authors have reviewed some HGNN methods, but one recent HGNN method is missing, i.e., *[TKDE 2021] Interpretable and Efficient Heterogeneous Graph Convolutional Network*.
  - For the training step and the test step, why the formula in Line 2 of Algorithm 1 has the adjacency matrix $A_{ts}$ while the formula in Line 2 of Algorithm 2 does not?
  - Is HMPNN an existing work? If so, it should be cited. The authors state that HMPNN merely extends the standard MPNN, but why does the message function (Eq. 2) in this work look different from the message function (Eq. 1) in MPNN? 
questions: Please respond to the weaknesses raised above.
limitations: The authors have well discussed the limitations and potential negative social impact of this work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper aims to transfer knowledge from the source node type with abundant labels to the target node type with zero labels on heterogeneous graphs by proposing a knowledge transfer network. In particular,
* The authors introduce the undiscovered problem of transferring knowledge from label-abundant nodes to zero-labeled nodes, which are connected via edges over the heterogeneous graph.
* The authors tackle this problem with a novel knowledge transfer network, which trains a feature transformation matrix that maps representations for target nodes with zero-label to the source node representation space.
* The authors propose a knowledge transfer network with a motivating theoretical observation that there exist transformation matrices to exactly map the target node representations to the source node embedding space, for predicting target zero-labeled nodes over the learned source-node representation space.
* The experimental results show that the proposed knowledge transfer network significantly outperforms the no domain adaptation and existing domain adaptation methods, by a large margin.
strengths_and_weaknesses: ### Strengths
* This paper tackles the highly realistic problem of zero-shot knowledge transfer from source to target nodes on a heterogeneous graph, which has, to my knowledge, not been introduced so far.
* The proposed method of representing zero-labeled target nodes in terms of label-abundant source nodes with the transformation matrices from target to source is convincing and highly interesting. I enjoy reading this method.
* The proposed architecture, namely knowledge transfer networks, is well-motivated by empirical observations to a toy heterogeneous graph, and by theoretical derivations to feature extraction modules on heterogeneous graph neural networks. 
* The performance improvements from baselines are significant and impressive.
* This paper is extremely well written and easy to follow. 

### Weaknesses
Note that the below points are not the major limitations of this work, but I would appreciate if the authors address them.
* The authors may include the work [1, 2] that considers transfer learning (or domain adaptation) problems for graph-structured data with graph neural networks, and perhaps more work on this topic of graph transfer learning could be discussed.
* The explanations of synthetic graph experiments in Section 4.2 and Section 6 are unclear. Also, the explanation in Appendix A.7, which contains full descriptions, is confusing as well. This might be because the setting for heterogeneous graphs differs from normal graphs, and I think it would be better to illustrate the authors' synthetic graph experimental setup with Figure.

---

[1] You et al. Handling Missing Data with Graph Representation Learning. NeurIPS 2020.

[2] Wu et al. Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. NeurIPS 2021.
questions: ### Major Questions and Suggestions
* The authors may discuss the relevant work on transfer learning or domain adaptation on graphs with graph neural networks (See the first weakness above).
* The authors should improve the explanations on synthetic graph experiments (See the second weakness above).
* The authors do not explain the task (e.g., P-A (L1)) in Table 1 and Table 2. I guess P-A denotes the Paper (Source) and Author (Target) as node types, however, the authors should define this.
* It would be interesting to see the performance on few-shot tasks, where few target nodes have their labels.

### Minor Questions and Suggestions
* The description of the first contribution in Line 76 is a little bit unclear. It is better to specify the term "cross-type transfer learning".
* Does the number of batchs in Figure 2 denote the number of iterations, or the batch size? It seems it would be the number of iterations, but the current term of the number of batchs is ambiguous.
* I am just curious that, instead of minimizing the loss for KTN for the representation of the last layer (i.e., L), why the authors do not minimize the KTN losses for all layers (i.e., 1, 2, 3, ..., L). 
* A caption for Table 2 is not informative.

### Typo
* Line 88: in in Section 3.
limitations: The authors provide the limitations and potential societal impact of their work. While the authors do not describe the *negative* societal impact, but only describe the positive point, this does not hurt the contributions of this work.
review_title: null

Meta-review: metareview: This paper proposes a transfer learning technique in heterogenous graphs, which can contain different types of nodes and edges. It identifies a limitation of existing work on graph neural networks for heterogenous graphs: they tend to implicitly learn separate representation encoders for each type of node. In other words, even though the same network is applied to all nodes, it tends to use separate activation paths to compute the representations. The proposed Knowledge Transfer Networks (KTNs) address this limitation with a novel architecture that explicitly learns a mapping between different domains (in this case, node types). Experiments on benchmark heterogeneous graphs show that KTNs lead to large improvements in classification accuracy on new node types.

The reviewers all liked the contributions of the paper, identifying the problem as important, the KTN architecture as novel, and the experimental results convincing. During the review process, the authors have already updated the paper several times in response to the reviewers feedback. In addition, they are encouraged to include the additional results with ALDA in the final version.

One remaining area for improvement is precisely scoping in the introduction and informal problem statements what exactly is meant by zero-shot learning in this context. As stated in the limitation statement, KTNs require that the task share the same label space. The authors have clarified this in section 3.3, but that is still too late for such a key definition.
confidence: Certain
Decision: Accept