Common use of Semi-Supervised Learning Without a Graph Clause in Contracts

Semi-Supervised Learning Without a Graph. Our robustness experiments show that GAM is effective even when the majority of edges in the graph connect nodes with mismatched labels. Therefore, we tested its power further by considering a more extreme scenario: no graph is provided, and the agreement model is tasked with learning whether an arbitrary pair of nodes shares a label. Note that having no graph, and picking random pairs of samples to use in the regularization terms in Equation 1, is equivalent to having a fully-connected graph from which we sample edges. We tested this scenario on ▇▇▇▇, Citeseer, and Pubmed and the results are marked as GAM* in Table 1. For completeness, we also show results for GCN+GAM* and GAT+GAM*, where even though the GAM* regularization term does not use the graph, the classification models use it by design. Our results show that GAM* also boosts the performance of all tested baseline models, with a gain of up to 19% accuracy for MLPs, 3.3% for GCNs, and 4.6% for GATs. It is worth noting that, even though GAM outperforms GAM* due to the extra information provided by the graph, GAM* generally outperforms the competing methods that also do not use a graph, and often even NGM which does. Non-graph Datasets. Since our approach no longer requires a graph to be provided, we tested GAM on the popular CIFAR-10 [16] and SVHN [26] datasets. For evaluation, we use the setup and train/validation/test splits provided by [27], which aims to provide a realistic framework for evaluating SSL methods. Thus, we start with 4000 and 1000 labeled samples for CIFAR-10 and SVHN, respectively, while the remaining training samples are considered unlabeled. More information about these datasets can be found in Appendix B. It is important to note that while ▇▇▇▇, Citeseer and Pubmed were evaluated under a transductive setting (where the input features and the graph structure of the test nodes are seen during training, but not their labels) as is typical in graph-based SSL, in the following experiments we evaluate GAM* under an inductive setting (the features of the test nodes are completely held out, and there is no graph to provide other information about them).