Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling

Last update: Aug 19, 2022

Related tags

Overview

Fast-Partial-Ranking-MNL

This repo provides a PyTorch implementation for the CopulaGNN models as described in the following paper:

Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling.

Jiaqi Ma*, Xingjian Zhang*, and Qiaozhu Mei. WSDM 2022.

(*: Equal contribution.)

Requirements

The code requires the following packages.

more_itertools==8.10.0
networkx==2.5.1
numpy==1.19.5
pandas==1.1.5
pyclustering==0.10.1.2
torch==1.9.0
tqdm==4.62.3

Example Commands to Run the Experiments

Learning single MNL from partial rankings on synthetic data

python3 dag_synthetic.py --num_classes 100 --num_samples 5000  # single MNL

Learning mixture of MNL from partial rankings on synthetic data

python3 dag_synthetic.py --num_classes 60 --num_samples 5000 --alphas [1,1,1]  --init_by_cluster # 3 MNLs with clustering based init

Network formation modeling of synthetic network data

python3 network_synthetic.py -r 0.5 -p 0.5 --fof --ua --pa --loss topk  # run full model with 4 components on a mixed (r,p)-graph

Network formation modeling of Flickr & Microsoft Academic Graph

cd source
wget -4 http://socialnetworks.mpi-sws.mpg.de/data/flickr-growth.txt.gz ../data/
python3 flickr_process.py # process flickr-growth.txt.gz, which is downloaded from http://socialnetworks.mpi-sws.mpg.de/data/flickr-growth.txt.gz
python3 flickr_train.py

# download mag_cli.csv by google drive
python3 mag_process.py  # process mag_cli.csv, which is downloaded from https://drive.google.com/file/d/17bgLs1iR96JW3Rd0mex3IK8qyU-qRElB/view?usp=sharing
python3 mag_train.py

Cite

@article{ma2022fast,
  title={Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling},
  author={Ma, Jiaqi and Zhang, Xingjian and Mei, Qiaozhu},
  journal={Proceedings of the 15th ACM International Conference on Web Search and Data Mining},
  year={2022}
}

Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling

Related tags

Overview

Fast-Partial-Ranking-MNL

Requirements

Example Commands to Run the Experiments

Cite

Owner

Xingjian Zhang

Dynamic Slimmable Network (CVPR 2021, Oral)

NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

Code for our paper: Online Variational Filtering and Parameter Learning

Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Computer Vision and Pattern Recognition, NUS CS4243, 2022

Deep Two-View Structure-from-Motion Revisited

This is a repository for a semantic segmentation inference API using the OpenVINO toolkit

PyTorch implementation and pretrained models for XCiT models. See XCiT: Cross-Covariance Image Transformer

A curated list of neural rendering resources.

CVPR 2021

A Survey on Deep Learning Technique for Video Segmentation

The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

ADB-IP-ROTATION - Use your mobile phone to gain a temporary IP address using ADB and data tethering

Gradient Step Denoiser for convergent Plug-and-Play

The official pytorch implemention of the CVPR paper "Temporal Modulation Network for Controllable Space-Time Video Super-Resolution".

Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks.

Toolchain to build Yoshi's Island from source code