3 citations · 4 across the 14 of their papers we have counts for
16 papers
Grounding Memory Summarization in Utility Intent
Zhenyu Lei, Mingjia Shi, Xingbo Fu +3
Existing summarizers for memory systems are typically optimized for human-facing criteria such as faithfulness, which misaligns with their true objective: preserving the evidence n…
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Zihan Chen, Song Wang, Xingbo Fu +4
The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. Ho…
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Xingbo Fu, Zhenyu Lei, Zihan Chen +3
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powe…
Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
Yuhan Yang, Xingbo Fu, Jundong Li
Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often remains between pre-training obj…
Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
Yiming Xu, Xu Hua, Zhen Peng +5
The widespread application of graph data in various high-risk scenarios has increased attention to graph anomaly detection (GAD). Faced with real-world graphs that often carry node…
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu, Zehong Wang, Zihan Chen +7
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent s…
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Zihan Chen, Xingbo Fu, Yushun Dong +2
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods us…
Graph Foundation Models: A Comprehensive Survey
Zehong Wang, Zheyuan Liu, Tianyi Ma +16
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural lang…
A Survey of Scaling in Large Language Model Reasoning
Zihan Chen, Song Wang, Zhen Tan +6
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. How…
Edge Prompt Tuning for Graph Neural Networks
Xingbo Fu, Yinhan He, Jundong Li
Pre-training powerful Graph Neural Networks (GNNs) with unlabeled graph data in a self-supervised manner has emerged as a prominent technique in recent years. However, inevitable o…
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong +3
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing m…
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…
Federated Few-shot Learning
Song Wang, Xingbo Fu, Kaize Ding +3
Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the…
Spatial-Temporal Networks for Antibiogram Pattern Prediction
Xingbo Fu, Chen Chen, Yushun Dong +4
An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understan…