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

Last update: Apr 02, 2021

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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

Owner

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

Related tags

Overview

README

Important Python Libraries

Instructions

File Organization

Graph Reversed Attention Network (GRAT)

Owner

Merlion: A Machine Learning Framework for Time Series Intelligence

On Out-of-distribution Detection with Energy-based Models

RoBERTa Marathi Language model trained from scratch during huggingface 🤗 x flax community week

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