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

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Diverse Object-Scene Compositions For Zero-Shot Action Recognition

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

Remote sensing change detection using PaddlePaddle

Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

Datasets, Transforms and Models specific to Computer Vision

Pytorch implementation of Compressive Transformers, from Deepmind

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

Implements a fake news detection program using classifiers.

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

Code for the paper "Unsupervised Contrastive Learning of Sound Event Representations", ICASSP 2021.

[LREC] MMChat: Multi-Modal Chat Dataset on Social Media

Research into Forex price prediction from price history using Deep Sequence Modeling with Stacked LSTMs.

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

Code for the ICML 2021 paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

A High-Level Fusion Scheme for Circular Quantities published at the 20th International Conference on Advanced Robotics

SberSwap Video Swap base on deep learning