Node Classification

Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explain…

Node Classification

PaSta: Noisy Node Classification with Partial Label Learning

2026-08-26 · Yujing Liu, Yixin Liu, Yu Zheng, Yue Tan 외 arxiv

Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. Howe…

Partial Label Learning Node Classification

ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning

2026-08-24 · Rui Xue, Tianfu Wu arxiv

Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on al…

Graph structure learning Representation Learning Node Classification Link Prediction

Fairness-Aware Network Embeddings: Methods, Applications, and Challenges

2026-08-19 · Ella Has, Harshith Kumar Yadav, Gaurav Dixit, Mykola Pechenizkiy 외 arxiv

Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks …

Representation Learning Graph Neural Network Node Classification Link Prediction

Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts

2026-08-06 · Quanxin Wang, Xuanting Xie, Bingheng Li, Xingtong Yu 외 arxiv

Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a single, fixed embedding space. In this …

Node Classification Link Prediction

Nonlinear Laplacians Improve Signed-Directed Graph Learning

2026-08-01 · Ali Parviz, Yuichi Yoshida arxiv

While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We int…

Node Classification Link Prediction Graph Learning

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