activity
20202026
most citedDeep Learning for Community Detection: Progress, Challenges and Opportunities

195 citations · 195 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training

Zhengyao Gu, Jonathan Light, Raul Astudillo +7

Post-training large foundation models with reinforcement learning typically relies on massive and heterogeneous datasets, making effective curriculum learning both critical and cha…

cs.LG2026

From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection

Yixin Liu, Shiyuan Li, Yu Zheng +4

Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…

cs.CL2026

Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval

Yibo Yan, Jiahao Huo, Guanbo Feng +12

With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich da…

cs.LG2024

Graph Neural Networks for Brain Graph Learning: A Survey

Xuexiong Luo, Jia Wu, Jian Yang +7

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology,…

cs.SI2020★ 195 cited

Deep Learning for Community Detection: Progress, Challenges and Opportunities

Fanzhen Liu, Shan Xue, Jia Wu +6

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and d…