activity
20242026
most citedLarge Language Models Meet Graph Neural Networks: A Perspective of Graph Mining

1 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning

Leheng Sheng, Yongtao Zhang, Wenchang Ma +6

While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradatio…

cs.SI2025

Analysis of Collaboration in CS Prizewinning with a Nobel-Turing Comparison

Boleslaw K. Szymanski, Yongtao Zhang, Brian Uzzi +1

In the scientific community, prizes play a pivotal role in shaping research trajectories by conferring credibility and offering financial incentives to researchers. Yet, we know li…

cs.LG2025

Virtual Width Networks

Seed, Baisheng Li, Banggu Wu +115

We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN d…

cs.LG20251 cited

Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation

Zhen Liu, Yongtao Zhang, Shaobo Ren +1

Graph domain adaptation has gained significant attention in label-scarce scenarios across different graph domains. Traditional approaches to graph domain adaptation primarily focus…

cs.CL20251 cited

Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

ByteDance Seed, :, Jiaze Chen +267

We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…

cs.LG20241 cited

Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining

Yuxin You, Zhen Liu, Xiangchao Wen +2

Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progres…