activity
20242026
collaborators

6 papers

cs.IR2026

TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation

Pengyu Zhang, Yangqin Jiang, Klim Zaporojets +2

Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates…

cs.CV2026

Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphs

Pengyu Zhang, Klim Zaporojets, Congfeng Cao +2

Multi-Modal Knowledge Graphs (MMKGs) enrich entities with multiple modalities such as text and images, yet entities with highly similar multi-modal features remain difficult to dis…

cs.LG2025

A Survey of Large Language Models for Data Challenges in Graphs

Mengran Li, Pengyu Zhang, Wenbin Xing +11

Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. While graph learning has a…

cs.SI2025

Implications of construction decisions in keyword-based networks: an empirical assessment

James Nevin, Salvatore Flavio Pileggi, Michael Lees +1

The large amounts of data continuously generated online offer opportunities to identify and analyse trends in various aspects of society. For instance, data from online social medi…

cs.LG2024

TIGER: Temporally Improved Graph Entity Linker

Pengyu Zhang, Congfeng Cao, Paul Groth

Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many u…

cs.LG2024

CYCLE: Cross-Year Contrastive Learning in Entity-Linking

Pengyu Zhang, Congfeng Cao, Klim Zaporojets +1

Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in…