The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
The zip folder contains 128 test networks created for testing clique-community detection in multiplex networks. Each test network is a folder with ten edgelists, all edgelists are sorted by nodes and are simple, undirected and unweighted. There is also a file 'true_cliques.csv' which contains set of nodes as (clique_id,node) indicating the membership of nodes to particular cliques, a clique is a complete subgraph and cliques are disjoint. The syntax for the networks looks like 160:1000:0.51:0.1:multiplex or 160:1000:0.51:0.1:2, the first format indicates the folder with all layers of the test network in the latter the last number indicates the layer count. The semicolon seperates configuration details, the first number 160 is the seed used for the random generator to create the individual layers of the multiplex network. The second number indicates the network size in terms of actors / nodes of a single layer thne 0.51 indicates the probability of a true edge being present in the layer, a true edge is an edge whose node pair is present in 'true_cliques.csv' within a single clique ID. that is also in the test network folder. 0.1 the second last number indicates the probability of non true edges (edges whose node pair is not in 'true_cliques.' within a single clique Id).
Thus each layer within a test network represents some kind of distortion of the ground truth network inferrable from 'true_cliques.csv' that consists only of disjoint complete subgraphs. This data set can be used to test algorithm that aim to recover that ground truth by using the layers of the multiplex network.
The dataset has been generated by the scala code 'TestNetworkGenerator.scala' and 'TestNetworkGenerator2.scala' in this repository https://github.com/LiquidIce95/networkscience_final_project_scala.git
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