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
State of the art faster Natural Language Processing in Tensorflow 2.0 .

tf-transformers: faster and easier state-of-the-art NLP in TensorFlow 2.0 ****************************************************************************

74 Dec 05, 2022
Translate U is capable of translating the text present in an image from one language to the other.

Translate U is capable of translating the text present in an image from one language to the other. The app uses OCR and Google translate to identify and translate across 80+ languages.

Neelanjan Manna 1 Dec 22, 2021
Anuvada: Interpretable Models for NLP using PyTorch

Anuvada: Interpretable Models for NLP using PyTorch So, you want to know why your classifier arrived at a particular decision or why your flashy new d

EDGE 102 Oct 01, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Highlights The strongest performances Tracker

Multimedia Research 485 Jan 04, 2023
This converter will create the exact measure for your cappuccino recipe from the grandiose Rafaella Ballerini!

About CappuccinoJs This converter will create the exact measure for your cappuccino recipe from the grandiose Rafaella Ballerini! Este conversor criar

Arthur Ottoni Ribeiro 48 Nov 15, 2022
Dual languaged (rus+eng) tool for packing and unpacking archives of Silky Engine.

SilkyArcTool English Dual languaged (rus+eng) GUI tool for packing and unpacking archives of Silky Engine. It is not the same arc as used in Ai6WIN. I

Tester 5 Sep 15, 2022
Augmenty is an augmentation library based on spaCy for augmenting texts.

Augmenty: The cherry on top of your NLP pipeline Augmenty is an augmentation library based on spaCy for augmenting texts. Besides a wide array of high

Kenneth Enevoldsen 124 Dec 29, 2022
Source code for CsiNet and CRNet using Fully Connected Layer-Shared feedback architecture.

FCS-applications Source code for CsiNet and CRNet using the Fully Connected Layer-Shared feedback architecture. Introduction This repository contains

Boyuan Zhang 4 Oct 07, 2022
Course project of [email protected]

NaiveMT Prepare Clone this repository git clone [email protected]:Poeroz/NaiveMT.git

Poeroz 2 Apr 24, 2022
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)

Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early

LEE YOON HYUNG 147 Dec 05, 2022
A workshop with several modules to help learn Feast, an open-source feature store

Workshop: Learning Feast This workshop aims to teach users about Feast, an open-source feature store. We explain concepts & best practices by example,

Feast 52 Jan 05, 2023
NLP made easy

GluonNLP: Your Choice of Deep Learning for NLP GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you l

Distributed (Deep) Machine Learning Community 2.5k Jan 04, 2023
Text Classification in Turkish Texts with Bert

You can watch the details of the project on my youtube channel Project Interface Project Second Interface Goal= Correctly guessing the classification

42 Dec 31, 2022
DLO8012: Natural Language Processing & CSL804: Computational Lab - II

NATURAL-LANGUAGE-PROCESSING-AND-COMPUTATIONAL-LAB-II DLO8012: NLP & CSL804: CL-II [SEMESTER VIII] Syllabus NLP - Reference Books THE WALL MEGA SATISH

AMEY THAKUR 7 Apr 28, 2022
A text augmentation tool for named entity recognition.

neraug This python library helps you with augmenting text data for named entity recognition. Augmentation Example Reference from An Analysis of Simple

Hiroki Nakayama 48 Oct 11, 2022
Stand-alone language identification system

langid.py readme Introduction langid.py is a standalone Language Identification (LangID) tool. The design principles are as follows: Fast Pre-trained

2k Jan 04, 2023
Voilà turns Jupyter notebooks into standalone web applications

Rendering of live Jupyter notebooks with interactive widgets. Introduction Voilà turns Jupyter notebooks into standalone web applications. Unlike the

Voilà Dashboards 4.5k Jan 03, 2023
Contains descriptions and code of the mini-projects developed in various programming languages

TexttoSpeechAndLanguageTranslator-project introduction A pleasant application where the client will be given buttons like play,reset and exit. The cli

Adarsh Reddy 1 Dec 22, 2021
✨Fast Coreference Resolution in spaCy with Neural Networks

✨ NeuralCoref 4.0: Coreference Resolution in spaCy with Neural Networks. NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolv

Hugging Face 2.6k Jan 04, 2023
Official Stanford NLP Python Library for Many Human Languages

Official Stanford NLP Python Library for Many Human Languages

Stanford NLP 6.4k Jan 02, 2023