A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

Related tags

Deep Learninganomaly-detectioncontrastive-learning
Overview

ANEMONE

A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

Dependencies

  • python==3.6.1
  • dgl==0.4.1
  • matplotlib==3.3.4
  • networkx==2.5
  • numpy==1.19.2
  • pyparsing==2.4.7
  • scikit-learn==0.24.1
  • scipy==1.5.2
  • sklearn==0.24.1
  • torch==1.8.1

To install all dependencies:

pip install -r requirements.txt

Usage

Anomalies have been injected into three datasets under the ./dataset directory. Please refer to GRAND-Lab/CoLA for graph anomaly injection.

To train and evaluate on Cora:

python run.py --expid 1 --device cuda:0 --runs 1 --alpha 0.8

To train and evaluate on Citeseer:

python run.py --dataset citeseer --expid 2 --device cuda:0 --runs 1 --alpha 0.6

To train and evaluate on Pubmed:

python run.py --dataset pubmed --expid 3 --device cuda:0 --runs 1 --alpha 0.8 --negsamp_ratio_patch 6 --negsamp_ratio_context 1

Citation

If you use our code in your research, please cite the following article:

@inproceedings{jin2021anomaly,
  title={ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning},
  author={Ming Jin and Yixin Liu and Yu Zheng and Lianhua Chi and Yuan-Fang Li and Shirui Pan},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  year={2021}
}
Owner
Graph Analysis & Deep Learning Laboratory, GRAND
GRaph ANalysis & Deep learning Laboratory (GRAND Lab) at Monash University
Graph Analysis & Deep Learning Laboratory, GRAND
GitHub Repository
The official codes of "Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners".

SSL models are Strong UDA learners Introduction This is the official code of paper "Semi-supervised Models are Strong Unsupervised Domain Adaptation L

Yabin Zhang 26 Dec 26, 2022
PyTorch implementation of Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Simple PyTorch Implementation of "Grokking" Implementation of Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Usage Running

Teddy Koker 15 Sep 29, 2022
Learning Off-Policy with Online Planning, CoRL 2021

LOOP: Learning Off-Policy with Online Planning Accepted in Conference of Robot Learning (CoRL) 2021. Harshit Sikchi, Wenxuan Zhou, David Held Paper In

Harshit Sikchi 24 Nov 22, 2022
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
Video Contrastive Learning with Global Context

Video Contrastive Learning with Global Context (VCLR) This is the official PyTorch implementation of our VCLR paper. Install dependencies environments

143 Dec 26, 2022
Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Marko Jocić 922 Dec 19, 2022
DiAne is a smart fuzzer for IoT devices

Diane Diane is a fuzzer for IoT devices. Diane works by identifying fuzzing triggers in the IoT companion apps to produce valid yet under-constrained

seclab 28 Jan 04, 2023
Categorical Depth Distribution Network for Monocular 3D Object Detection

CaDDN CaDDN is a monocular-based 3D object detection method. This repository is based off of [OpenPCDet]. Categorical Depth Distribution Network for M

Toronto Robotics and AI Laboratory 289 Jan 05, 2023
LSTM-VAE Implementation and Relevant Evaluations

LSTM-VAE Implementation and Relevant Evaluations Before using any file in this repository, please create two directories under the root directory name

Lan Zhang 5 Oct 08, 2022
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be

Ziniu Hu 346 Dec 19, 2022
OneShot Learning-based hotword detection.

EfficientWord-Net Hotword detection based on one-shot learning Home assistants require special phrases called hotwords to get activated (eg:"ok google

ANT-BRaiN 102 Dec 25, 2022
PyTorch implementation of GLOM

GLOM PyTorch implementation of GLOM, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attent

Yeonwoo Sung 20 Aug 17, 2022
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi

Jie Ren 17 Dec 12, 2022
ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

ALFRED A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han,

ALFRED 204 Dec 15, 2022
pq is a jq-like Pickle file viewer

pq PQ is a jq-like viewer/processing tool for pickle files. howto # pq '' file.pkl {'other': 456, 'test': 123} # pq 'table' file.pkl |other|test| | 45

3 Mar 15, 2022
a curated list of docker-compose files prepared for testing data engineering tools, databases and open source libraries.

data-services A repository for storing various Data Engineering docker-compose files in one place. How to use it ? Set the required settings in .env f

BigData.IR 525 Dec 03, 2022
TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments for IV 2022.

TorchGRL TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffi

XXQQ 42 Dec 09, 2022
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Dense Passage Retrieval Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the

Meta Research 1.1k Jan 03, 2023
The Power of Scale for Parameter-Efficient Prompt Tuning

The Power of Scale for Parameter-Efficient Prompt Tuning Implementation of soft embeddings from https://arxiv.org/abs/2104.08691v1 using Pytorch and H

Kip Parker 208 Dec 30, 2022
Implementing DeepMind's Fast Reinforcement Learning paper

Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.

Marcus Chiam 6 Nov 28, 2022