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Xiao Huang

43 papers hereh-index 242k citations94 works total

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

author position
  • middle author17
  • last author23

Across the 40 of 43 papers where every author was matched, so the position is known.

fields
  • cs.CL20
  • cs.LG8
  • cs.IR7
  • cs.AI4
  • cs.CV2
  • cs.DB1
same name
  • Xiao Huang — 22 papers, h 25
  • Xiao Huang — 10 papers, h 27
  • Xiao Huang — 8 papers, h 20
  • Xiao Huang — 8 papers, h 4
  • Xiao Huang — 5 papers, h 10
  • Xiao Huang — 4 papers, h 4

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
20192026
most citedImproving Generalizability of Graph Anomaly Detection Models via Data Augmentation

41 citations · 195 across the 42 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides

Yuzhi Wang, Rongjun Ye, Shengyuan Chen +5

Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated…

cs.AI2026

Graph-based Agent Memory: Taxonomy, Techniques, and Applications

Chang Yang, Chuang Zhou, Yilin Xiao +15

Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…

cs.AI2024

CLR-Bench: Evaluating Large Language Models in College-level Reasoning

Junnan Dong, Zijin Hong, Yuanchen Bei +3

Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LL…

cs.AI2024

Neuro-Symbolic Entity Alignment via Variational Inference

Shengyuan Chen, Zheng Yuan, Qinggang Zhang +3

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic…

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