FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.

Related tags

Deep Learning fastCover
Overview

README

Code for the paper :

FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.

Implementation of an integrated efficient framework solving k-budget constrained d-dominating set problem (k-dDSP).

Link: TBA.

More real-world test graphs (5.88G) are found in Dropbox: https://www.dropbox.com/s/nm2ilieqf8axq2e/data.zip?dl=0.

Important Python Libraries

  • igraph=0.9.1
  • torch=1.8.1
  • dgl=0.6.0 (based on the CUDA version)
  • furl=2.0.0
  • timeout-decorator=0.5.0

Instructions

File Organization

  • data/graphs/: Some small graphs for training and test
  • model/: Loss functions and GNN layers (implemented in dgl)
  • experiments/: Training and evaluation launchers.
  • baselines/: Heuristic algorithms for

Graph Reversed Attention Network (GRAT)

To train a GRAT solving k-dDSP, run the demo experiments/train_models.py.

To evaluate the model, run exeriments/evaluate_models.py

You might also like...
Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Seulki Park 70 Jan 3, 2023
constructing maps of intellectual influence from publication data
constructing maps of intellectual influence from publication data

Influencemap Project @ ANU Influence in the academic communities has been an area of interest for researchers. This can be seen in the popularity of a

CS Metrics 13 Jun 18, 2022
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.
Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization

Benjamin Biggs 29 Dec 28, 2022
RE3: State Entropy Maximization with Random Encoders for Efficient Exploration

State Entropy Maximization with Random Encoders for Efficient Exploration (RE3) (ICML 2021) Code for State Entropy Maximization with Random Encoders f

Younggyo Seo 47 Nov 29, 2022
This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis, accepted at EMNLP 2021.
This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis, accepted at EMNLP 2021.

MultiModal-InfoMax This repository contains the official implementation code of the paper Improving Multimodal Fusion with Hierarchical Mutual Informa

Deep Cognition and Language Research (DeCLaRe) Lab 89 Dec 26, 2022
Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

Microsoft 282 Jan 9, 2023
[CVPR 2021]
[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Codes for this paper The Lottery Tickets Hypo

VITA 59 Dec 28, 2022
Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models
Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

null 4 Jul 12, 2021
A self-supervised 3D representation learning framework named viewpoint bottleneck.

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

null 63 Aug 11, 2022
Owner
null
GitHub
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 7, 2022
Hunt down social media accounts by username across social networks

Hunt down social media accounts by username across social networks Installation | Usage | Docker Notes | Contributing Installation # clone the repo $

null 1 Dec 14, 2021
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 3, 2022
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 7, 2022
The implement of papar "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization"

SIGIR2021-EGLN The implement of paper "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization" Neural graph based Col

null 15 Dec 27, 2022
Joint learning of images and text via maximization of mutual information

mutual_info_img_txt Joint learning of images and text via maximization of mutual information. This repository incorporates the algorithms presented in

Ruizhi Liao 10 Dec 22, 2022
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 2, 2023
A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

Sea AI Lab 62 Nov 8, 2022
Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Hold me tight! Influence of discriminative features on deep network boundaries This is the source code to reproduce the experiments of the NeurIPS 202

EPFL LTS4 19 Dec 10, 2021