
Code The file 'code.zip' contains the source code used for training and testing. To train new or use existing models, we recommend compiling the source code using the included Apptainer recipes. See README.md for more information on how to use them. To train a model with the same configuration and hyperparameters we used in the paper, we suggest opening the '*.hparams' file in a text editor: it is just a json file and contains the arguments used to initialize the model. In general, each hyperparameter corresponds to an argument to the training program. For example: "pair_embeddings": true must be enabled to train the more expressive models, and "composition_depth": 0 is the t parameter in the paper. Some arguments override others, e.g. "all_compositions": true is used for the baseline R-GNN_2, to use all possible compositions rather than those based on t. Data The file 'data.zip' contains all training and test instances used in the paper. Models The file 'models.zip' contains all the models that are used in the paper.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
