These are the datasets used in Towards Better Evaluation for Dynamic Link Prediction For preparing the datasets, we closely follow the baseline methods' data preparation strategy. 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 are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline). Ater preprocessing 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.