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

Code for CVPR 2021 oral paper "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts"

Object Detection using YOLO from PyImageSearch

A whale detector design for the Kaggle whale-detector challenge!

WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction"

Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

TensorFlow Implementation of "Show, Attend and Tell"

Source code for deep symbolic optimization.

QuALITY: Question Answering with Long Input Texts, Yes!

Code for paper "Learning to Reweight Examples for Robust Deep Learning"

Anagram Generator in Python

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

Pytorch implementation of the paper Time-series Generative Adversarial Networks

Supervised Classification from Text (P)

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

Code of Adverse Weather Image Translation with Asymmetric and Uncertainty aware GAN

SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

The Turing Change Point Detection Benchmark: An Extensive Benchmark Evaluation of Change Point Detection Algorithms on real-world data

Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

L-Verse: Bidirectional Generation Between Image and Text