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Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples


Core Concepts
The core message of this paper is to propose a novel heterogeneous graph contrastive learning method MEOW, which constructs a coarse view and a fine-grained view for contrast based on meta-paths, and learns hard-valued weights for negative samples to better distinguish them. The authors further propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples.
Abstract
The paper introduces a heterogeneous graph contrastive learning method called MEOW. The key highlights are: Coarse view and fine-grained view construction: The coarse view reflects which objects are connected by meta-paths. The fine-grained view utilizes meta-path contexts and characterizes how objects are connected by meta-paths. Weighted negative samples: The authors recognize the limitation of the InfoNCE loss in distinguishing negative samples. They propose a contrastive loss function with weighted negative samples to better distinguish false negatives and hard negatives. The weights are determined by node clustering results, where nodes in the same cluster are considered as false negatives and assigned smaller weights, while nodes in different clusters are treated as hard negatives and assigned larger weights. Prototypical contrastive learning: To further improve the model performance, the authors introduce prototypical contrastive learning, where the cluster centers are used as positive/negative samples to learn compact embeddings for nodes in the same cluster. Adaptive negative sample weights: The authors propose a variant model AdaMEOW, which adaptively learns soft-valued weights of negative samples using a two-layer MLP, making negative samples more personalized and improving the learning ability of node representations. The authors conduct extensive experiments on four benchmark heterogeneous information network datasets, demonstrating the superiority of MEOW and AdaMEOW over other state-of-the-art methods in node classification and node clustering tasks.
Stats
The dataset contains 4019 papers, 7167 authors, and 60 subjects in the ACM dataset. The DBLP dataset contains 4057 authors, 14328 papers, 20 conferences, and 7723 terms. The Aminer dataset contains 6564 papers, 13329 authors, and 35890 references. The IMDB dataset contains 4275 movies, 5432 actors, 2083 directors, and 7313 keywords.
Quotes
"To further enrich the information of HINs, nodes are usually associated with labels. Since object labeling is costly, graph neural networks (GNNs) have recently been applied for classifying nodes in HINs and have shown to achieve superior performance." "Contrastive learning aims to construct positive and negative pairs for contrast, following the principle of maximizing the mutual information (MI) between positive pairs while minimizing that between negative pairs." "We recognize the limitation of the InfoNCE loss based on theoretical analysis and propose a contrastive loss function with weighted negative samples to better distinguish negative samples."

Deeper Inquiries

How can the proposed MEOW and AdaMEOW models be extended to handle dynamic heterogeneous graphs, where the graph structure and node features may evolve over time

To extend the MEOW and AdaMEOW models to handle dynamic heterogeneous graphs, where the graph structure and node features may evolve over time, several modifications and enhancements can be implemented. Adaptive Learning Rate: Incorporating an adaptive learning rate mechanism can help the models adjust to changes in the graph structure and node features. By dynamically updating the learning rate based on the rate of change in the graph, the models can adapt more effectively to evolving data. Incremental Training: Implementing incremental training techniques can allow the models to continuously learn from new data without retraining the entire model. This way, the models can stay up-to-date with the evolving graph dynamics. Temporal Embeddings: Introducing temporal embeddings for nodes and edges can capture the time-dependent relationships in the graph. By considering the temporal aspect, the models can better understand the evolution of the graph over time. Online Learning: Implementing online learning strategies can enable the models to update their parameters in real-time as new data streams in. This way, the models can adapt to changes in the graph structure and node features as they occur.

What other types of negative sample weighting schemes can be explored beyond the clustering-based approach used in this paper

There are several alternative negative sample weighting schemes that can be explored beyond the clustering-based approach used in the paper: Distance-based Weighting: Assigning weights to negative samples based on their distance from the anchor node can be a viable approach. Closer negative samples can be assigned higher weights, while farther ones can have lower weights. Similarity-based Weighting: Weighting negative samples based on their similarity to the anchor node can also be effective. Negative samples that are more similar to the anchor can be assigned higher weights. Confidence-based Weighting: Utilizing a confidence measure to assign weights to negative samples can be beneficial. Negative samples that the model is more confident about being misclassified can be given higher weights. Graph Structure-based Weighting: Leveraging the graph structure to assign weights to negative samples can be explored. Negative samples that disrupt the graph structure or connectivity can be assigned higher weights.

Can the meta-path context information be further leveraged to guide the construction of the coarse and fine-grained views, beyond the current aggregation and fusion mechanisms

To further leverage the meta-path context information to guide the construction of the coarse and fine-grained views, beyond the current aggregation and fusion mechanisms, the following strategies can be considered: Attention Mechanisms: Implementing attention mechanisms can allow the models to focus on specific parts of the meta-path context that are more relevant for node representation learning. By attending to different aspects of the meta-path context, the models can extract more informative features. Graph Neural Networks: Utilizing more advanced graph neural network architectures can help capture the intricate relationships within the meta-path context. Graph neural networks can effectively model the complex dependencies and interactions present in the meta-path context. Semantic Embeddings: Generating semantic embeddings from the meta-path context can provide a richer representation of how nodes are connected. By embedding the meta-path context information, the models can capture more nuanced relationships between nodes. Hierarchical Aggregation: Implementing a hierarchical aggregation strategy for the meta-path context can capture multi-level dependencies. By aggregating information at different levels of granularity, the models can extract more comprehensive features from the meta-path context.
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