Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

Last update: Nov 27, 2022

Related tags

Overview

PSML

paper: Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

PSML_IONE,PSML_ABNE,PSML_DEEPLINK,PSML_SNNA:

numpy==1.14<br>
networkx==2.0<br>
scipy==0.19.1<br>
tensorflow>=1.12.1<br>
gensim==3.0.1<br>
scikit-learn==0.19.0<br>

PSML_DALUAP,PSML_MGCN:

python >= 3.6<br>
pytorch >= 0.4<br>
numpy  1.18.0<br>
tqdm<br>
networkx >2.0<br>

support data comes from :https://github.com/ChuXiaokai/CrossMNA
query data comes from :https://github.com/ColaLL/IONE
, https://github.com/ColaLL/AcrossNetworkEmbeddingDiversity

For PSML_IONE

   first run PSML_IONE.py
   second run Four.py
   getPrecision--should run emd_to_ione_emd.py and emd_to_ione_emd_t.py

For PSML_ABNE

   first run PSML_ABNE.py
   second run Four.py
   getPrecision--should run emd_to_ione_emd.py and emd_to_ione_emd_t.py

For PSML_SNNA

   use deepwalk or line get pre_data
   run PSML_SNNA.py

For PSML_DeepLink

   run embedding.py to use word2vec get pre_data
   run PSML_Deeplink.py

For PSML_MGCN

   run PSML_MGCN.py

For PSML_DALUAP

   run PSML_DALUAP.py

NOTE:

Method of adding pseudo node, Take two pseudo anchors, which are connected to each other, such as subnetwork file:

        node      node
         1          2
         3(anchor)  4

You need change it to:

        node      node
         1          2
         3(anchor)  4
         3          5(pse)
         3          6(pse)
         5(pse)     3
         6(pse)     3
         5(pse)     6(pse)
         6(pse)     5(pse)

Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

Related tags

Overview

PSML

paper: Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

For PSML_IONE

For PSML_ABNE

For PSML_SNNA

For PSML_DeepLink

For PSML_MGCN

For PSML_DALUAP

NOTE:

Owner

Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

This repository is dedicated to developing and maintaining code for experiments with wide neural networks.

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

A package to predict protein inter-residue geometries from sequence data

realsense d400 -> jpg + csv

Deploy pytorch classification model using Flask and Streamlit

PyTorch reimplementation of hand-biomechanical-constraints (ECCV2020)

[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

wmctrl ported to Python Ctypes

Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS of first stage is 3.42 and second stage is 3.47.

Dense Unsupervised Learning for Video Segmentation (NeurIPS*2021)

Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.

How Do Adam and Training Strategies Help BNNs Optimization? In ICML 2021.

基于Flask开发后端、VUE开发前端框架，在WEB端部署YOLOv5目标检测模型

A python interface for training Reinforcement Learning bots to battle on pokemon showdown

Space Ship Simulator using python

Behavioral "black-box" testing for recommender systems

PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC