activity
20232025
most citedCausality and Independence Enhancement for Biased Node Classification

11 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection

Boshen Shi, Yongqing Wang, Fangda Guo +3

Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-…

cs.AI20241 cited

M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark

Wei Song, Yadong Li, Jianhua Xu +8

As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these mod…

cs.LG202311 cited

Causality and Independence Enhancement for Biased Node Classification

Guoxin Chen, Yongqing Wang, Fangda Guo +4

Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selec…

cs.SI20234 cited

Significant-attributed Community Search in Heterogeneous Information Networks

Yanghao Liu, Fangda Guo, Bingbing Xu +3

Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities w…

cs.AI2023

OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning

Boshen Shi, Yongqing Wang, Fangda Guo +3

Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two…