Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
arXiv:2604.05732v1 Announce Type: new Abstract: Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods have been proposed to learn graph structures and downstream tasks simultaneously, existing methods are predominantly designed for homogeneous graphs, while GSL for heterogeneous graphs remains largely unexplored. Two challenges arise in this context. Firstly, the quality of the input graph structure has a more profound impact on GNN-based heterogeneous GRL models compared to their homogeneous counterparts. Secondly, most existing homogenous GRL models encounter memory consumption issues when applied directly to heterogeneous graphs. In this paper, we propose a novel Graph Topology learning Enhanced Heterogeneous Graph Representation Learning framework (ToGRL).ToGRL learns high-quality graph
arXiv:2604.05732v1 Announce Type: new Abstract: Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods have been proposed to learn graph structures and downstream tasks simultaneously, existing methods are predominantly designed for homogeneous graphs, while GSL for heterogeneous graphs remains largely unexplored. Two challenges arise in this context. Firstly, the quality of the input graph structure has a more profound impact on GNN-based heterogeneous GRL models compared to their homogeneous counterparts. Secondly, most existing homogenous GRL models encounter memory consumption issues when applied directly to heterogeneous graphs. In this paper, we propose a novel Graph Topology learning Enhanced Heterogeneous Graph Representation Learning framework (ToGRL).ToGRL learns high-quality graph structures and representations for downstream tasks by incorporating task-relevant latent topology information. Specifically, a novel GSL module is first proposed to extract downstream task-related topology information from a raw graph structure and project it into topology embeddings. These embeddings are utilized to construct a new graph with smooth graph signals. This two-stage approach to GSL separates the optimization of the adjacency matrix from node representation learning to reduce memory consumption. Following this, a representation learning module takes the new graph as input to learn embeddings for downstream tasks. ToGRL also leverages prompt tuning to better utilize the knowledge embedded in learned representations, thus enhancing adaptability to downstream tasks. Extensive experiments on five real-world datasets show that our ToGRL outperforms state-of-the-art methods by a large margin.
Executive Summary
The article presents ToGRL, a novel framework for Graph Topology enhanced Heterogeneous Graph Representation Learning (GRL) that addresses key challenges in improving the performance of GNN-based models on noisy, real-world heterogeneous graphs. By decoupling the optimization of the adjacency matrix from node representation learning, ToGRL mitigates memory consumption issues while enhancing downstream task performance. The framework leverages task-relevant latent topology information to construct refined graphs and employs prompt tuning to better utilize learned representations. Empirical validation on five datasets demonstrates superior performance over existing state-of-the-art methods, underscoring its potential to advance GRL in heterogeneous graph contexts.
Key Points
- ▸ ToGRL introduces a two-stage Graph Structure Learning (GSL) approach that separates topology optimization from representation learning, addressing memory inefficiencies in homogeneous GSL models when applied to heterogeneous graphs.
- ▸ The framework incorporates latent topology embeddings derived from downstream task-relevant signals to construct smoother, task-optimized graph structures, thereby mitigating the adverse effects of noisy input graphs on GNN performance.
- ▸ Prompt tuning is integrated to enhance the adaptability of learned representations to downstream tasks, improving model generalization and interpretability.
- ▸ Extensive experiments across five real-world datasets validate ToGRL’s superiority over existing methods, achieving significant performance gains in heterogeneous GRL.
Merits
Novelty and Theoretical Rigor
ToGRL introduces a paradigm shift in heterogeneous GRL by decoupling adjacency matrix optimization from representation learning, addressing a critical gap in existing GSL methods designed predominantly for homogeneous graphs. The integration of topology embeddings and prompt tuning further enhances the framework’s adaptability and performance.
Practical Efficacy
The empirical validation demonstrates substantial performance improvements over state-of-the-art methods, highlighting ToGRL’s potential for real-world applications in domains like social network analysis, recommendation systems, and bioinformatics, where heterogeneous graphs are prevalent.
Scalability and Memory Efficiency
By separating the optimization processes, ToGRL mitigates memory consumption issues inherent in direct applications of homogeneous GSL models to heterogeneous graphs, making it more scalable for large-scale graph datasets.
Demerits
Computational Overhead
The two-stage approach and integration of prompt tuning may introduce additional computational overhead, particularly during the topology embedding extraction phase, which could limit its feasibility for time-sensitive or resource-constrained applications.
Dependency on Task Relevance
The effectiveness of ToGRL is contingent on the quality and relevance of the latent topology information extracted. Poorly defined task relevance or noisy input signals could degrade the performance gains, undermining the framework’s robustness in certain scenarios.
Limited Generalization to Homogeneous Graphs
While ToGRL is designed for heterogeneous graphs, its performance in homogeneous graph contexts remains unexamined. The framework’s adaptability to such graphs is unclear, potentially limiting its broader applicability.
Expert Commentary
ToGRL represents a significant advancement in the field of heterogeneous graph representation learning, addressing longstanding challenges in GNN performance on real-world, noisy graphs. The decoupling of adjacency matrix optimization from representation learning is a particularly innovative approach, mitigating the memory inefficiencies that have plagued homogeneous GSL models when applied to heterogeneous contexts. The integration of task-relevant topology embeddings and prompt tuning further enhances the framework’s adaptability, offering a robust solution to the adaptability and generalization challenges in GRL. While the empirical results are compelling, the framework’s computational overhead and dependency on task relevance warrant further investigation. Additionally, exploring ToGRL’s applicability to homogeneous graphs could broaden its impact. Overall, ToGRL sets a new benchmark for heterogeneous GRL and paves the way for future research into scalable, task-optimized graph representation learning methods.
Recommendations
- ✓ Further research should investigate the computational efficiency of ToGRL, particularly in optimizing the topology embedding extraction phase to reduce overhead and enhance scalability for time-sensitive applications.
- ✓ Future work could explore the integration of adaptive prompt tuning mechanisms that dynamically adjust to varying task relevance, thereby improving the framework’s robustness in diverse application scenarios.
- ✓ The authors should extend the empirical validation of ToGRL to include homogeneous graphs to assess its broader applicability and performance across different graph types.
- ✓ Collaboration with domain experts in fields like bioinformatics or social network analysis could provide real-world case studies to validate ToGRL’s practical efficacy and identify potential limitations in specific applications.
Sources
Original: arXiv - cs.LG