NCN4MAG: Deploying the Neural Citation Network (NCN) on the Microsoft Academic Graph (MAG)

Description

Pipeline for training the Neural Citation Network (NCN) with the Microsoft Academic Graph (MAG) dataset.

To setup the processing service, see https://github.com/sebastiancelis98/CitationRexApp This Repository is for the training part only.

• input: MAG-dump (.tsv for each table in the MAG - tab seperated text files)

  • needed files: Papers (to get year), PaperAuthorAffiliations (to get authors), Authors (to get author names), Paperurls (to filter language), PaperFieldsOfStudy (to filter for CS papers), PaperCitationContexts (to get citations)

• output: weights for the model

How to run it:

Pipeline (using bwUniCluster)

1. clone this repository
2. place dump in workspace (run $ws_allocate _name 10 to create a new workspace with more than enough temporary storage for 10 days. Copy relevant files to your home directory)
3. create virtualenvironment and install all dependencies from req.txt ($pip3 install -r req.txt)
4. download nlp model: python -m spacy download en_core_web_lg
5. create seperate file for faster computation: run cut -f1,20 Papers.txt > Papers+Counts.txtin the directory where the MAG dump is placed
6. to run each, execute $sbatch --partition=insert_node_here job.sh after specifying the needed memory and the file to execute in job.sh (also change the path in job.sh to match the created venv)

Jobs to run:

• data preparation: (you can also execute all data.py functions in one job, but that takes more estimated time = more time before the job is accepted by the HPC Cluster)

  • pandas_all.py (partition: gpu_8, 600g memory, 7 hours runtime)
    • parameters: set all paths to the input files
    • lower_bound_citationcount is the number of citations a paper needs to be considered as "cited paper"
    • creates mag_all.txt
  • ncn/data.py (function get_mag_data) (partition: gpu_4, 200g memory, 1 hour runtime)
    • creates mag_subset.txt
  • ncn/data.py (function prepare_mag_data) (partition: gpu_4, 50g memory, 2 hour runtime)
    • creates mag_data.csv (not used for training but for creating the title - author dicts used fr the webservice)
  • ncn/data.py (function split mag_data) (partition_ gpu_4, 30g memory, 2 hours runtime)
    • creates mag_train.csv, mag_valid.csv, mag_test.csv

• training:

  • training.py (parameters: authors=false, for faster training maybe hidden_size=128 (default: 256)
    • bwUniCluster ressources on our dataset, running on gpu_4:
      • CPU Utilized: 1-05:32:00
      • Job Wall-clock time: 1-05:48:35 (30 hours)
      • Memory Utilized: 19.01 GB
    • creates train_losses, valid_losses, and most importantly: The weight of the trained model: NCN_DATETIME.pt

Tips when working on bwUniCluster

• $ws_list gives the location of your workspace(s)
• $scontrol show jobid _insert_id gives the current status of your job
• when running the python jobs $python3 -u xyz.py makes the StdOut Stream unbuffered, so you can have print statements which will print to the job's slurm_jobid.out file.
• for more documentation see https://wiki.bwhpc.de/e/Main_Page