Code for: Uncovering simultaneous breakthroughs with a robust measure of disruptiveness
Munjung Kim, Sadamori Kojaku, Yong‐Yeol Ahn · arXiv (Cornell University) · 2025
Disruption This archive contains a snapshot of the code used in the accompanying manuscript "Uncovering simultaneous breakthroughs with a robust measure of disruptiveness." The original development repository is hosted on GitHub at https://github.com/yy/embedding-disruptiveness. pip package We developed a pip package for calculating the embedding disurptiveness measure. You can install it by pip install embedding-disruptiveness The source code of the package is in here, and the example code for calculating embedding disruptiveness measure with the package is in notebooks/embedding-disruptiveness package.ipynb. Environment setup uv If you are using uv, run the following to install necessary packages: uv venv source .venv/bin/activate uv pip install -r requirements.txt Snakemake Since the current python code requires many parameters, it would be better to use snakemake tool. Once a container has been created, you can execute the following commands in the workflow directory: snakemake '{working_dirctory}/data/original/150_5_q_1_ep_5_bs_4096_embedding/distance.npy' -j This command will locate the rule for generating the {working_directory}/data/original/150_5_q_1_ep_5_bs_4096_embedding/distance.npy file, which corresponds to the calculating_distance rule in our Snakefile. This rule takes the {working_directory}/data/original/150_5_q_1_ep_5_bs_4096_embedding/in.npy and {working_directory}/data/original/150_5_q_1_ep_5_bs_4096_embedding/out.npy files as inputs, and Snakemake will execute the rule responsible for generating these files, named as the embedding_all_networkrule in our Snakefile. The embedding_all_network rule executes the following command: python3 scripts/Embedding.py {input} {params.Dsize} {params.Window} {params.Device1} {params.Device2} {params.Name} {params.q} {params.epoch} {params.batch}. The parameters for this command are defined within the embedding_all_network rule as follows: params: Name = "{network_name}", Dsize = "{dim}", Window = "{win}", Device1 = "6", Device2 = "7", q = "{q}", epoch = "{ep}", batch = "{bs}" Without Snakemake Without snakemake, you can follow the following steps. Embedding Calculation To calculate the embedding vectors, you can use the following command line: python3 scripts/Embedding.py {path/to/citation_network_file} {embedding_dimension} {window_size} {device1} {device2} {citation_network_name} {q_value} {epoch_size} {batch_size} For example, you can run the command as shown below: python3 scripts/Embedding.py /data/original/citation_net.npz 200 5 6 7 original 1 5 1024 Embedding.py will train the node2vec model and save the result of in-vectors under the path {path/to/citation_network_file}/{DIM}_{WIN}_q_{Q}_ep_{EPOCH}_bs_{BATCH}_embedding/. For instance, the above command will save in and out vectors in the path /data/original/200_5_q_1_ep_5_bs_1024_embedding/in.npy and /data/original/200_5_q_1_ep_5_bs_1024_embedding/out.npy. Distance Calculation Based on the embedding vectors you calculate from the above process, you can execute the following command to calculate the distance. python3 scripts/Distance_Disruption.py distance {path/to/invectors} {path/to/outvectors} {path/to/citation_network_file} {device name} For example, you can run the command as shown below: python3 scripts/Distance_Disruption.py distance /data/original/ {path/to/outvectors} {path/to/citation_network_file} {device name}