19 citations · 20 across the 64 of their papers we have counts for
57 papers · 1 filter
TopoCompile: Molecular Generation with Exact Global Count Constraints
Xiang Wu, Rong-Hua Li, Quanqing Xu +2
Molecular generation designs novel molecules with desired structures and properties. Global count constraints specify molecular size, elemental composition, and ring content withou…
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…
GraphSelect for Budgeted Representation Selection in Multimodal Graph Inference
Xu Wang, Xunkai Li, Yinlin Zhu +1
Multimodal graph predictors combine text, images, and relations to classify connected entities. How much of this input is needed to preserve their predictions? We study budgeted re…
MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning
Kairui Yang, Ziheng Yi, Xunkai Li +4
Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approac…
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…
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…
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…
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…
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…
GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
Xu Wang, Xunkai Li, Yinlin Zhu +2
Multimodal retrieval uses images, text, and object relationships to answer different questions about the same collection. A query may seek an object's paired description, another o…
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
Sirui Zhang, Haonan Wang, Xunkai Li +5
Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applicat…
STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
Zekai Chen, Xun Wu, Xunkai Li +3
Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. As graph data increasingly contain multimodal node attributes such as text and…
OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
Chenxi Wan, Xunkai Li, Yilong Zuo +6
Multimodal-Attributed Graph (MAG) learning has achieved remarkable success in modeling complex real-world systems by integrating graph topology with rich attributes from multiple m…
Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
Sicheng Liu, Xunkai Li, Daohan Su +4
Graph Foundation Models (GFMs) have achieved remarkable success in generalizing across diverse domains. However, they mainly focus on Text-Attributed Graphs (TAGs), leaving Multimo…
DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis
Ru Zhang, Xunkai Li, Yaxin Deng +7
Recently, data-centric AI methodology has been a dominant paradigm in single-cell transcriptomics analysis, which treats data representation rather than model complexity as the fun…
OpenDDI: A Comprehensive Benchmark for DDI Prediction
Xinmo Jin, Bowen Fan, Xunkai Li +9
Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computa…
OptiMAG: Structure-Semantic Alignment via Unbalanced Optimal Transport
Yilong Zuo, Xunkai Li, Zhihan Zhang +3
Multimodal Attributed Graphs (MAGs) have been widely adopted for modeling complex systems by integrating multi-modal information, such as text and images, on nodes. However, we ide…