The current study presents a collection of synthetic network databases intended for evaluating classification methods. The first database, termed "4-models," comprises synthetic networks that have been generated based on four distinct network classes, namely, Random, Small-world, Scale-free, and Geographical. This database includes 2800 networks for each class, resulting in a total of 11,200 networks. The second database, named "Scale-free," is composed of scale-free synthetic models generated using the models proposed by Barabási and Albert and by Dorogovstev and Mendes. This database includes five classes, comprising four Barabasi models based on the network's power law parameter and a Mendes model, each class consisting of 100 networks. The third database, named "Noisy," is created by modifying the topology of the networks from the 4-models and Scale-free databases via the addition or removal of edges. This database contains three sub-datasets, based on the level of randomness added to the networks, and eight different classes, each of which includes 100 networks. To ensure consistency and comparability of our results, we implemented a standardization procedure whereby all databases were converted to adjacency list format. This allowed us to more efficiently process and analyze the data.