SoftEdge: Regularizing Graph Classification with Random Soft Edges
Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning. Due to their effectiveness, simple edge and node manipulations (e.g., addition and deletion) have been widely used in graph augmentation. Nevertheless, such common augmentation techniques can dramatically change the semantics of the original graph, causing overaggressive augmentation and thus under-fitting in the GNN learning. To address this problem arising from dropping or adding graph edges and nodes, we propose SoftEdge, which assigns random weights to a portion of the edges of a given graph for augmentation. The synthetic graph generated by SoftEdge maintains the same nodes and their connectivities as the original graph, thus mitigating the semantic changes of the original graph. We empirically show that this simple method obtains superior accuracy to popular node and edge manipulation approaches and notable resilience to the accuracy degradation with the GNN depth.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationData AugmentationGraph ClassificationSimilar Papers 제목 키워드 기반
Generalized Spectral Clustering for Directed and Undirected Graphs
Spectral clustering is a popular approach for clustering undirected graphs, but its extension to directed graphs (digraphs) is much more challenging. A typical workaround is to naively symmetrize the adjacency matrix of …
Clusteringgraph partitioningAdUE: Improving uncertainty estimation head for LoRA adapters in LLMs
Uncertainty estimation remains a critical challenge in adapting pre-trained language models to classification tasks, particularly under parameter-efficient fine-tuning approaches such as adapters. We introduce AdUE1, an …
parameter-efficient fine-tuningMeta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning wit…
Contrastive LearningGraph LearningMeta-LearningNode ClassificationOverfitting and Generalizing with (PAC) Bayesian Prediction in Noisy Binary Classification
We consider a PAC-Bayes type learning rule for binary classification, balancing the training error of a randomized ''posterior'' predictor with its KL divergence to a pre-specified ''prior''. This can be seen as an exten…
Binary ClassificationRegularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reconstruction is to solve the underdetermined inverse problem independently over time and space. Here we present how the conventional approach can be e…