Graph neural networks ({GNNs}) are among the most widely used methods for node classification in graphs. A common strategy to improve their predictive performance is to enrich nodes with additional features. A weakness of this method is that the set of appropriate features can vary from graph to graph. We address this shortcoming by proposing a novel method. In a preprocessing step, a first {GNN} is trained on a set of graphs with varying structural properties, using a candidate set of node features fixed in advance. The resulting {GNN} model is then used to predict the most relevant features from the candidate set for unseen target graphs, which are later processed for node classification. For each target graph, a second {GNN} is trained on the graph, which is enriched with the node feature vectors calculated for the features selected by the first {GNN}. A key advantage of the proposed method is that the features are selected without computing the candidate features for the target graph. Our experimental results on synthetic and real-world graphs show that even a few features selected in this way is sufficient to significantly improve the predictive performance of {GNNs} that use either none or all of the candidate features. Moreover, the time needed to learn the second {GNN} for the target graph can be reduced by up to two orders of magnitude.<\/p>\n","protected":false},"excerpt":{"rendered":"
Graph neural networks ({GNNs}) are among the most widely used methods for node classification in graphs. A common strategy to improve their predictive performance is to enrich nodes with additional features. A weakness of this method is that the set of appropriate features can vary from graph to graph. We address this shortcoming by proposing a novel method. In a preprocessing step, a first {GNN} is trained on a set […]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32265","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32265\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32265"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}