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Xinming Zhang

15 papers hereh-index 8314 citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author6

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.CL3
  • cs.IR3
  • cs.AI1
same name
  • Xinming Zhang — 10 papers, h 3
  • Xinming Zhang — 5 papers, h 1
  • Xinming Zhang — 3 papers, h 5
  • Xinming Zhang — 2 papers, h 1
  • Xinming Zhang — 1 paper, h 6
  • Xinming Zhang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedA Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

4 citations · 13 across the 15 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.IR2026

Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation

Jie Luo, Qi Jin, Xinming Zhang

Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing fra…

cs.LG2026

TradingMoE: Routing the Right Experts in Evolving Markets

Chang Zhou, Xingtong Yu, Minbin Huang +4

Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can…

cs.AI2026

GraphReAct: Reasoning and Acting for Multi-step Graph Inference

Xingtong Yu, Zhongwei Kuai, Chang Zhou +6

Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…

cs.CL2026

Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights

Xingtong Yu, Shenghua Ye, Ruijuan Liang +4

Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.