BiNE: Bipartite Network Embedding

Related tags

Text Data & NLPBiNE
Overview

BiNE: Bipartite Network Embedding

This repository contains the demo code of the paper:

BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiangnan He & Aoying Zhou

which has been accepted by SIGIR2018.

Note: Any problems, you can contact me at [email protected]. Through email, you will get my rapid response.

Environment settings

  • python==2.7.11
  • numpy==1.13.3
  • sklearn==0.17.1
  • networkx==1.11
  • datasketch==1.2.5
  • scipy==0.17.0
  • six==1.10.0

Basic Usage

Main Parameters:

Input graph path. Defult is '../data/rating_train.dat' (--train-data)
Test dataset path. Default is '../data/rating_test.dat' (--test-data)
Name of model. Default is 'default' (--model-name)
Number of dimensions. Default is 128 (--d)
Number of negative samples. Default is 4 (--ns)
Size of window. Default is 5 (--ws)
Trade-off parameter $\alpha$. Default is 0.01 (--alpha)
Trade-off parameter $\beta$. Default is 0.01 (--beta)
Trade-off parameter $\gamma$. Default is 0.1 (--gamma)
Learning rate $\lambda$. Default is 0.01 (--lam)
Maximal iterations. Default is 50 (--max-iters)
Maximal walks per vertex. Default is 32 (--maxT)
Minimal walks per vertex. Default is 1 (--minT)
Walk stopping probability. Default is 0.15 (--p)
Calculate the recommendation metrics. Default is 0 (--rec)
Calculate the link prediction. Default is 0 (--lip)
File of training data for LR. Default is '../data/wiki/case_train.dat' (--case-train)
File of testing data for LR. Default is '../data/wiki/case_test.dat' (--case-test)
File of embedding vectors of U. Default is '../data/vectors_u.dat' (--vectors-u)
File of embedding vectors of V. Default is '../data/vectors_v.dat' (--vectors-v)
For large bipartite, 1 do not generate homogeneous graph file; 2 do not generate homogeneous graph. Default is 0 (--large)
Mertics of centrality. Default is 'hits', options: 'hits' and 'degree_centrality' (--mode)

Usage

We provide two processed dataset:

  • DBLP (for recommendation). It contains:

    • A training dataset ./data/dblp/rating_train.dat
    • A testing dataset ./data/dblp/rating_test.dat

  • Wikipedia (for link prediction). It contains:

    • A training dataset ./data/wiki/rating_train.dat
    • A testing dataset ./data/wiki/rating_test.dat

  • Each line is a instance: userID (begin with 'u')\titemID (begin with 'i') \t weight\n

    For example: u0\ti0\t1

Please run the './model/train.py'

cd model
python train.py --train-data ../data/dblp/rating_train.dat --test-data ../data/dblp/rating_test.dat --lam 0.025 --max-iter 100 --model-name dblp --rec 1 --large 2 --vectors-u ../data/dblp/vectors_u.dat --vectors-v ../data/dblp/vectors_v.dat

The embedding vectors of nodes are saved in file '/model-name/vectors_u.dat' and '/model-name/vectors_v.dat', respectively.

Example

Recommendation

Run

cd model
python train.py --train-data ../data/dblp/rating_train.dat --test-data ../data/dblp/rating_test.dat --lam 0.025 --max-iter 100 --model-name dblp --rec 1 --large 2 --vectors-u ../data/dblp/vectors_u.dat --vectors-v ../data/dblp/vectors_v.dat

Output (training process)

======== experiment settings =========
alpha : 0.0100, beta : 0.0100, gamma : 0.1000, lam : 0.0250, p : 0.1500, ws : 5, ns : 4, maxT :  32, minT : 1, max_iter : 100
========== processing data ===========
constructing graph....
number of nodes: 6001
walking...
walking...ok
number of nodes: 1177
walking...
walking...ok
getting context and negative samples....
negative samples is ok.....
context...
context...ok
context...
context...ok
============== training ==============
[*************************************************************************************************** ]100.00%

Output (testing process)

============== testing ===============
recommendation metrics: F1 : 0.1132, MAP : 0.2041, MRR : 0.3331, NDCG : 0.2609

Link Prediction

Run

cd model
python train.py --train-data ../data/wiki/rating_train.dat --test-data ../data/wiki/rating_test.dat --lam 0.01 --max-iter 100 --model-name wiki --lip 1 --large 2 --gamma 1 --vectors-u ../data/wiki/vectors_u.dat --vectors-v ../data/wiki/vectors_v.dat --case-train ../data/wiki/case_train.dat --case-test ../data/wiki/case_test.dat

Output (training process)

======== experiment settings =========
alpha : 0.0100, beta : 0.0100, gamma : 1.0000, lam : 0.0100, p : 0.1500, ws : 5, ns : 4, maxT :  32, minT : 1, max_iter : 100, d : 128
========== processing data ===========
constructing graph....
number of nodes: 15000
walking...
walking...ok
number of nodes: 2529
walking...
walking...ok
getting context and negative samples....
negative samples is ok.....
context...
context...ok
context...
context...ok
============== training ==============
[*************************************************************************************************** ]100.00%

Output (testing process)

============== testing ===============
link prediction metrics: AUC_ROC : 0.9468, AUC_PR : 0.9614
Owner
leihuichen
student
leihuichen
GitHub Repository
Client library to download and publish models and other files on the huggingface.co hub

huggingface_hub Client library to download and publish models and other files on the huggingface.co hub Do you have an open source ML library? We're l

Hugging Face 644 Jan 01, 2023
A look-ahead multi-entity Transformer for modeling coordinated agents.

baller2vec++ This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling

Michael A. Alcorn 30 Dec 16, 2022
Official codebase for Can Wikipedia Help Offline Reinforcement Learning?

Official codebase for Can Wikipedia Help Offline Reinforcement Learning?

Machel Reid 82 Dec 19, 2022
Outreachy TFX custom component project

Schema Curation Custom Component Outreachy TFX custom component project This repo contains the code for Schema Curation Custom Component made as a par

Robert Crowe 5 Jul 16, 2021
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Introduction Funnel-Transformer is a new self-attention model that gradually compresses the sequence of hidden states to a shorter one and hence reduc

GUOKUN LAI 197 Dec 11, 2022
Precision Medicine Knowledge Graph (PrimeKG)

PrimeKG Website | bioRxiv Paper | Harvard Dataverse Precision Medicine Knowledge Graph (PrimeKG) presents a holistic view of diseases. PrimeKG integra

Machine Learning for Medicine and Science @ Harvard 103 Dec 10, 2022
BiNE: Bipartite Network Embedding

BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiang

leihuichen 214 Nov 24, 2022
The PyTorch based implementation of continuous integrate-and-fire (CIF) module.

CIF-PyTorch This is a PyTorch based implementation of continuous integrate-and-fire (CIF) module for end-to-end (E2E) automatic speech recognition (AS

Minglun Han 24 Dec 29, 2022
spaCy plugin for Transformers , Udify, ELmo, etc.

Camphr - spaCy plugin for Transformers, Udify, Elmo, etc. Camphr is a Natural Language Processing library that helps in seamless integration for a wid

342 Nov 21, 2022
Constituency Tree Labeling Tool

Constituency Tree Labeling Tool The purpose of this package is to solve the constituency tree labeling problem. Look from the dataset labeled by NLTK,

张宇 6 Dec 20, 2022
Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA

Named Entity Recognition API with spaCy and GiNZA I wrote a blog post about this

Yuki Okuda 3 Feb 27, 2022
Repositório da disciplina no semestre 2021-2

Avisos! Nenhum aviso! Compiladores 1 Este é o Git da disciplina Compiladores 1. Aqui ficará o material produzido em sala de aula assim como tarefas, w

6 May 13, 2022
This repository contains the codes for LipGAN. LipGAN was published as a part of the paper titled "Towards Automatic Face-to-Face Translation".

LipGAN Generate realistic talking faces for any human speech and face identity. [Paper] | [Project Page] | [Demonstration Video] Important Update: A n

Rudrabha Mukhopadhyay 438 Dec 31, 2022
An IVR Chatbot which can exponentially reduce the burden of companies as well as can improve the consumer/end user experience.

IVR-Chatbot Achievements 🏆 Team Uhtred won the Maverick 2.0 Bot-a-thon 2021 organized by AbInbev India. ❓ Problem Statement As we all know that, lot

ARYAMAAN PANDEY 9 Dec 08, 2022
:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.

Dedupe Python Library dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on

Dedupe.io 3.6k Jan 02, 2023
DVC-NLP-Simple-usecase

dvc-NLP-simple-usecase DVC NLP project Reference repository: official reference repo DVC STUDIO MY View Bag of Words- Krish Naik TF-IDF- Krish Naik ST

SUNNY BHAVEEN CHANDRA 2 Oct 02, 2022
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow

This Repository contains a sample code for Tacotron 2, WaveGlow with multi-speaker, emotion embeddings together with a script for data preprocessing.

Ivan Didur 106 Jan 01, 2023
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.

Transformer Embedder A Word Level Transformer layer based on PyTorch and 🤗 Transformers. How to use Install the library from PyPI: pip install transf

Riccardo Orlando 27 Nov 20, 2022
Segmenter - Transformer for Semantic Segmentation

Segmenter - Transformer for Semantic Segmentation

592 Dec 27, 2022
A multi-lingual approach to AllenNLP CoReference Resolution along with a wrapper for spaCy.

Crosslingual Coreference Coreference is amazing but the data required for training a model is very scarce. In our case, the available training for non

Pandora Intelligence 71 Jan 04, 2023