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20242026
most citedInvariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.IR2026

Think2Go: Generative Next POI Recommendation with LLM Reasoning

Zhuang Zhuang, Shanshan Feng, Hangwei Qian +4

Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next de…

cs.LG20261 cited

Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

Lanting Fang, Yulian Yang, Yawei Zhang +3

Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems. However, most existing CTDG representation learning methods are tailored to in-d…

cs.LG2026

Text-attributed Graph Condensation via Text Selection and Attribute Matching

Haowei Han, Yuxiang Wang, Guojia Wan +5

Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and languag…

cs.SI2026

Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization

Hongliang Qiao, Shanshan Feng, Min Zhou +5

The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diff…

cs.AI2025

The Digital Ecosystem of Beliefs: does evolution favour AI over humans?

David M. Bossens, Shanshan Feng, Yew-Soon Ong

As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understa…

cs.NE2025

Language Model Evolutionary Algorithms for Recommender Systems: Benchmarks and Algorithm Comparisons

Jiao Liu, Zhu Sun, Shanshan Feng +2

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functiona…