It is therefore difficult to discuss and understand what choices, among the high-levelalgorithmic options as well as low-levelimplementation details, matter.

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Decision making in these systems has a long history, yet, if the state is not fully observed acting optimally in such systems is notoriously hard.

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In this paper, we first propose a flexible framework for safe screening based on the Fenchel-Rockafellar duality and then deriveastrong safe screening rule for norm-regularized least squares using the proposed framework.

InCM-GCL, we can either takethe textfeaturexT orthe image featurexI asthe content feature, and consider the corresponding text encoderfT or image encoderfI as the content encoder. In this section, we discuss the settings of baseline models for imbalanced node classification over fourgraphs. G1: We convert the rich text content into the bag-of-words feature vectors, and further feed the feature vectors with different imbalance ratios to a two-layer MLP [7] classifier to get the classification results. For AMiner, YelpChi, and GitHub graph datasets, we implement CHI-Square [11]toselect useful feature words. G2: We implement three graph neural network based representation learning models including GCN [5], GAT [9], and GraphSAGE [2] to learn the node embeddings by leveraging both node feature (bag-of-words feature vector) andgraph structure information.