Homophily-enhanced Structure Learning for Graph Clustering

Gu, Ming; Yang, Gaoming; Zhou, Sheng; Ma, Ning; Chen, Jiawei; Tan, Qiaoyu; Liu, Meihan; Bu, Jiajun

doi:10.1145/3583780.3614915

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The first is the discrepancies with regard to the assumptions applied to common graphs and road networks. Most previous graph representation learning methods predominantly target citation graphs [5], [6] or social networks [7], [8], devising techniques grounded in certain wellestablished assumptions specific to these types of graphs. These assumptions, however, may not be applicable or valid for road networks.…”

Section: Introductionmentioning

confidence: 99%

Robust Road Network Representation Learning

Chen

Cong

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance of various time-sensitive downstream tasks. Specifically, we propose to encode two pivotal semantic characteristics intrinsic to road networks: traffic patterns and traveling semantics. To achieve this, we refine the skip-gram module by incorporating auxiliary objectives aimed at predicting the traffic context associated with a target road segment. Moreover, we leverage trajectory data and design pre-training strategies based on Transformer to distill traveling semantics on road networks. DyToast further augments this framework by employing unified trigonometric functions characterized by their beneficial properties, enabling the capture of temporal evolution and dynamic nature of road networks more effectively. With these proposed techniques, we can obtain representations that encode multi-faceted aspects of knowledge within road networks, applicable across both road segment-based applications and trajectory-based applications. Extensive experiments on two real-world datasets across three tasks demonstrate that our proposed framework consistently outperforms the state-of-the-art baselines by a significant margin.

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Section: Introductionmentioning

confidence: 99%

Robust Road Network Representation Learning

Chen

Cong

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

Choi,

Kim,

Kim

et al. 2024

Proceedings of the ACM Web Conference 2024

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We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsalbased GCL methods select the most representative nodes for each class and store them in a replay buffer for later use in training subsequent tasks. However, we discovered that considering only the class representativeness of each replayed node makes the replayed nodes to be concentrated around the center of each class, incurring a potential risk of overfitting to nodes residing in those regions, which aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach heavily relies on a few replayed nodes to retain knowledge obtained from previous tasks, involving the replayed nodes that have irrelevant neighbors in the model training may have a significant detrimental impact on model performance. In this paper, we propose a GCL model named DSLR, specifically, we devise a coverage-based diversity (CD) approach to consider both the class representativeness and the diversity within each class of the replayed nodes. Moreover, we adopt graph structure learning (GSL) to ensure that the replayed nodes are connected to truly informative neighbors. Extensive experimental results demonstrate the effectiveness and efficiency of DSLR. Our source code is available at https://github.com/seungyoon-Choi/DSLR_official.

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Homophily-enhanced Structure Learning for Graph Clustering

Cited by 2 publications

References 21 publications

Robust Road Network Representation Learning

Robust Road Network Representation Learning

DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning

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