1 citations · 1 across the 32 of their papers we have counts for
33 papers
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…
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…
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…
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…
TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
The negative sampling strategy can effectively train collaborative filtering (CF) recommendation models based on implicit feedback by constructing positive and negative samples. Ho…
A Topology-Aware Positive Sample Set Construction and Feature Optimization Method in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling strategies are widely used in implicit collaborative filtering to address issues like data sparsity and class imbalance. However, these methods often introduce fa…
A Simple yet Effective Negative Sampling Plugin for Constructing Positive Sample Pairs in Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Most implicit collaborative filtering (CF) models are trained with negative sampling, where existing work designs sophisticated strategies for high-quality negatives while largely…
Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
Jiayi Wu, Zhengyu Wu, Xunkai Li +2
Negative sampling is a pivotal technique in implicit collaborative filtering (CF) recommendation, enabling efficient and effective training by contrasting observed interactions wit…
LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
Xunkai Li, Zhengyu Wu, Zekai Chen +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…
FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
Zhengyu Wu, Yinlin Zhu, Xunkai Li +4
Foundation models have shown remarkable cross-domain generalization in language and vision, inspiring the development of graph foundation models (GFMs). However, existing GFMs typi…
Toward General Digraph Contrastive Learning: A Dual Spatial Perspective
Zhengyu Wu, Daohan Su, Yang Zhang +3
Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information. However, existing methods…
Federated Graph Unlearning
Yuming Ai, Xunkai Li, Jiaqi Chao +5
The demand for data privacy has led to the development of frameworks like Federated Graph Learning (FGL), which facilitate decentralized model training. However, a significant oper…
A Comprehensive Data-centric Overview of Federated Graph Learning
Zhengyu Wu, Xunkai Li, Yinlin Zhu +8
In the era of big data applications, Federated Graph Learning (FGL) has emerged as a prominent solution that reconcile the tradeoff between optimizing the collective intelligence b…
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
Xunkai Li, Zhengyu Wu, Kaichi Yu +4
The directed graph (digraph), as a generalization of undirected graphs, exhibits superior representation capability in modeling complex topology systems and has garnered considerab…
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Zhengyu Wu, Boyang Pang, Xunkai Li +6
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the…
Federated Prototype Graph Learning
Zhengyu Wu, Xunkai Li, Yinlin Zhu +3
In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigat…
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
Xunkai Li, Bowen Fan, Zhengyu Wu +3
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in…
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
Xunkai Li, Daohan Su, Zhengyu Wu +4
The -parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node featu…
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
Zhengyu Wu, Guang Zeng, Huilin Lai +7
Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preservi…
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu +4
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration…
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Xunkai Li, Yinlin Zhu, Boyang Pang +7
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inher…