19 citations · 20 across the 73 of their papers we have counts for
86 papers
OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains
Xunkai Li, Miao Zhang, Zekai Chen +6
Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms of agent organization. Exis…
Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach
Xunkai Li, Chenxi Wan, Yinlin Zhu +4
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-At…
No-Free-Graph: Learning When Multimodal Data Should Be Graphified
Zekai Chen, Kai Hu, YuXin Zeng +6
Multimodal graph learning has recently emerged as an effective paradigm for in corporating inter-entity relationships into multimodal representations. Existing studies have made su…
Measuring the Influence of Modality-Specific Node Representations on Graphs
Xu Wang, Xunkai Li, Yinlin Zhu +1
Multimodal attributed graphs combine graph structure with node text and image representations in recommendation and multimedia analysis. Graph predictors propagate these representa…
Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models
Zekai Chen, Xun Wu, Hailin Zhang +7
Federated multimodal graph foundation models (GFMs) aim to adapt pretrained multimodal models to decentralized graph data, where each client owns a private multimodal graph and can…
MOVE: Multimodal Open-world Verification and Expansion for Graph Learning
Zekai Chen, Jiayang Xing, Xun Wu +7
Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Existing approaches typically d…
MACE: Memory-Agent Co-Evolution with Adaptive Memory Graphs for Multi-Agent Systems
Kairui Yang, Minghao An, Xunkai Li +4
LLM-based multi-agent systems generate collaboration traces that record how agents plan tasks, verify intermediate results, and repair failures. Reusing these procedures requires p…
OpenMAS-GCom. A Diagnostic Benchmark for Graph-enhanced Multi-Agent Systems
Kairui Yang, Xunkai Li, Kaixiang Zhang +4
Graph-enhanced multi-agent systems (G-MAS) coordinate large language model agents through communication graphs and role assignments, which determine how agents exchange information…
Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach
Sirui Zhang, Yubing Zhou, Xunkai Li +6
Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure and cross-modality attributes t…
PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation
Haokun Deng, Xunkai Li, Hongchao Qin +1
Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read: narrow retrieval can miss…
LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
Xunkai Li, Zekai Chen, Zhengyu Wu +6
Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This…
SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +3
Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods…
Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Yinlin Zhu, Di Wu, Yi Zhang +5
Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopte…
MMFGU: Multimodal Federated Graph Unlearning
Haodong Lu, Zekai Chen, Weiwei Ji +5
Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data. However,…
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Zekai Chen, Haodong Lu, Shihao Li +5
Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging class…
OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
Yuze Dai, Zhihan Zhang, Yan Zhao +6
Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world TAGs are often imperfect, with quality iss…
Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
Xunkai Li, Guohao Fu, Yuming Ai +4
Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-co…
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
Zekai Chen, Kairui Yang, Xuaner Chen +4
Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-cen…
Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
Zhengyu Wu, Hongchao Qin, Xunkai Li +3
MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance…
SMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
Zhengyu Wu, Xu Wang, Hongchao Qin +4
Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities. Traditional graph learning contextualizes node semantics by c…
Multimodal Graph Negative Learning
Zhengyu Wu, Xu Wang, Hongchao Qin +4
Multimodal attributed graphs (MAGs) integrate graph topology with heterogeneous modality attributes, such as text and images, thereby enabling richer modeling of complex relational…
MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA
Yilong Zuo, Xunkai Li, Jing Yuan +3
Long-document multimodal question answering requires a system to locate sparse evidence in long PDFs and integrate clues from text, tables, images, charts, and complex layouts. Exi…
Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphs
Sirui Zhang, Xu Wang, Zhengyu Wu +2
Multimodal Attributed Graphs (MAGs) model real-world entities by coupling graph topology with heterogeneous attributes such as text and images. They support graph-centric tasks req…
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
Zekai Chen, Miao Zhang, Jiayang Xing +4
Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modali…