yixinliu233

User data from Github https://github.com/yixinliu233

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Monash University

Melbourne, Australia

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TrustAGI-Lab

Yixin Liu's repositories

GREET

[AAAI'23] Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Language:PythonMIT50 2 2

D2PT

[KDD'23] Learning Strong Graph Neural Networks with Weak Information

Language:PythonMIT43 1 2

G-OOD-D

[WSDM'23] GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

Language:PythonMIT38 3 3

SIGNET

[NeurIPS'23] Towards Self-Interpretable Graph-Level Anomaly Detection

Language:PythonMIT23 2 1

ARC

[NeurIPS'24] ARC: A Generalist Graph Anomaly Detector with In-Context Learning

Language:Python16 2 2

SimpDM

[CIKM'24] Self-Supervision Improves Diffusion Models for Tabular Data Imputation

Language:PythonMIT14 10

TADDY_pytorch

A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

Language:PythonMIT500

SUBLIME

[WWW'22] Towards Unsupervised Deep Graph Structure Learning

Language:PythonMIT400

CoLA

[TNNLS'21] Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Language:PythonMIT300

FedStar

[AAAI'23] Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

Language:Python300

ANEMONE

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

Language:PythonMIT200

Awesome-Deep-Graph-Anomaly-Detection

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.

MIT100

SL-GAD

A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection", TKDE-21

Language:PythonMIT100

yixinliu233.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Language:JavaScriptMIT000