activity
20142026
most citedCommunity Detection via Local Dynamic Interaction

9 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

A Lightweight Plastic-Memory Framework for Graph Few-Shot Class-Incremental Learning

Zihan Mei, Zhili Qin, Tongze Zhang +3

Graph Incremental Learning has garnered increasing attention as dynamic graph data continues to emerge across diverse fields. Conventional approaches primarily address catastrophic…

cs.AR2026

SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving

Hongyuan Liu, Yawei Li, Zhiqiang Que +3

Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost, yet even state-of-the-art 4-…

cs.CV2026

The Geometry of Compromise: Unlocking Generative Capabilities via Controllable Modality Alignment

Hongyuan Liu, Qinli Yang, Wen Li +6

Vision-Language Models (VLMs) such as CLIP learn a shared embedding space for images and text, yet their representations remain geometrically separated, a phenomenon known as the m…

cs.LG2024

Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning

Jiaming Liu, Hongyuan Liu, Zhili Qin +4

The dynamic nature of open-world scenarios has attracted more attention to class incremental learning (CIL). However, existing CIL methods typically presume the availability of com…

cs.CV2023

Open-world Semi-supervised Novel Class Discovery

Jiaming Liu, Yangqiming Wang, Tongze Zhang +3

Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more comple…

cs.SI2016★ 2 cited

Graph Clustering with Density-Cut

Junming Shao, Qinli Yang, Jinhu Liu +1

How can we find a good graph clustering of a real-world network, that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new g…

cs.SI2014★ 9 cited

Community Detection via Local Dynamic Interaction

Junming Shao, Zhichao Han, Qinli Yang

How can we uncover the natural communities in a real network that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new commu…