Skyorca

Boshen Shi's repositories

OpenGDA

(CIKM'23 resource track) Graph Domain Adaptation Benchmark for Cross-network Learning

Language:PythonMIT1300

Ref2Bib

给定一个参考文献列表，爬取谷歌学术生成bib文件

Language:Python500

ChatPaperX

An improved version of ChatPaper, which automatically download papers from arxiv and summarize through chatgpt

Language:PythonNOASSERTION200

JHGDA

Official code for CIKM2023 paper 'Improving Graph Domain Adaptation with Network Hierarchy'

Language:Python200

ACDNE

Adversarial Deep Network Embedding for Cross-network Node Classification

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ACT

The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).

Language:Python000

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.

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