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

Code for the paper "Relation of the Relations: A New Formalization of the Relation Extraction Problem"

Sequence-to-Sequence learning using PyTorch

Principled Detection of Out-of-Distribution Examples in Neural Networks

Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

PyTorch-Multi-Style-Transfer - Neural Style and MSG-Net

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

OpenDILab Multi-Agent Environment

Transfer-Learn is an open-source and well-documented library for Transfer Learning.

一个免费开源一键搭建的通用验证码识别平台，大部分常见的中英数验证码识别都没啥问题。

Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation.

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

Application of K-means algorithm on a music dataset after a dimensionality reduction with PCA

My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

ALBERT-pytorch-implementation - ALBERT pytorch implementation

Code for ICML 2021 paper: How could Neural Networks understand Programs?

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Implementation of FSGNN

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.