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

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021))

Predict and time series avocado hass

Lightwood is Legos for Machine Learning.

Simulating an AI playing 2048 using the Expectimax algorithm

TensorFlow implementation of original paper : https://github.com/hszhao/PSPNet

Fine-tune pretrained Convolutional Neural Networks with PyTorch

PolyTrack: Tracking with Bounding Polygons

The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

A tight inclusion function for continuous collision detection

Noether Networks: meta-learning useful conserved quantities

CVPR 2020 oral paper: Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax.

Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

Large scale embeddings on a single machine.

Localization Distillation for Object Detection

PyTorch implementation of ''Background Activation Suppression for Weakly Supervised Object Localization''.

Code for "Unsupervised State Representation Learning in Atari"

Metric learning algorithms in Python

This repo is for segmentation of T2 hyp regions in gliomas.

modelvshuman is a Python library to benchmark the gap between human and machine vision

a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version