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Siwei Wang

14 papers hereh-index 201k citations84 works total

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

author position
  • middle author14

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • cs.LG4
  • cs.IR3
  • cs.AI1
  • cs.CL1
same name
  • Siwei Wang — 13 papers, h 6
  • Siwei Wang — 6 papers, h 44
  • Siwei Wang — 4 papers, h 4
  • Siwei Wang — 4 papers, h 3
  • Siwei Wang — 3 papers, h 2
  • Siwei Wang — 2 papers, h 2

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

works on
credit assignment 1graph-structured retrieval 1question answering 1reinforcement learning 1search agents 1

From the 1 of 14 linked papers with an AI index.

activity
20242026
collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Parameter-Free Clustering via Self-Supervised Consensus Maximization (Extended Version)

Lijun Zhang, Suyuan Liu, Siwei Wang +4

Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, lim…

cs.LG2026

Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification

Jiaxin Zhang, Yiqi Wang, Siwei Wang +4

Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, e…

cs.LG2026★ 19 cited

Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets

Meng Liu, Ke Liang, Siwei Wang +3

Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the…

cs.LG2024

Test-Time Training on Graphs with Large Language Models (LLMs)

Jiaxin Zhang, Yiqi Wang, Xihong Yang +6

Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…

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