| ## Descriptions of data files in the paper *Towards Better Evaluation for Dynamic Link Prediction* | |
| The original networks are saved as <network>.csv. | |
| The networks are formatted as follows: | |
| * Each edge is denoted in one line. | |
| * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features. | |
| * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task. | |
| * The first line denotes the network format. | |
| * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges. | |
| The network edge-lists can be pre-processed by ```preprocess_data/preprocess_data.py```. | |
| After pre-processing the network edge-list, there are three files that are used by the models: | |
| * <ml_network>.csv: this file contains the timestamp edge-list. | |
| * <ml_network>.npy: this file contains the edge features in the dense `npy` format that has the features in binary format. | |
| * <ml_network_node>.npy: this file contains the node features in the dense `npy` format that contains the node features in binary format. | |
| Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods. | |