64 citations · 122 across the 16 of their papers we have counts for
17 papers
UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
Taoran Fang, Yan Deng, Chunping Wang +3
With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such het…
PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization
Renhong Huang, Ning Tang, Jiarong Xu +6
Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recomme…
How to Use Graph Data in the Wild to Help Graph Anomaly Detection?
Yuxuan Cao, Jiarong Xu, Chen Zhao +4
In recent years, graph anomaly detection has found extensive applications in various domains such as social, financial, and communication networks. However, anomalies in graph-stru…
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
Hanyang Yuan, Jiarong Xu, Renhong Huang +3
Graph neural networks (GNNs) have attracted considerable attention due to their diverse applications. However, the scarcity and quality limitations of graph data present challenges…
Can Modifying Data Address Graph Domain Adaptation?
Renhong Huang, Jiarong Xu, Xin Jiang +2
Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to d…
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
Hanyang Yuan, Jiarong Xu, Cong Wang +4
The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studi…
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
Yifei Sun, Qi Zhu, Yang Yang +4
Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generall…
Towards Fair Graph Federated Learning via Incentive Mechanisms
Chenglu Pan, Jiarong Xu, Yue Yu +5
Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current ef…
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Jiarong Xu, Renhong Huang, Xin Jiang +4
Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for downstream tasks with unlabeled data, and it has recently become an active research area. The…
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Haoran Deng, Yang Yang, Jiahe Li +3
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changi…
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
Yuxuan Cao, Jiarong Xu, Carl Yang +5
In recent years, graph pre-training has gained significant attention, focusing on acquiring transferable knowledge from unlabeled graph data to improve downstream performance. Desp…
Universal Prompt Tuning for Graph Neural Networks
Taoran Fang, Yunchao Zhang, Yang Yang +2
In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field…
Neural Topological Ordering for Computation Graphs
Mukul Gagrani, Corrado Rainone, Yang Yang +7
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In thi…
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Xuanwen Huang, Yang Yang, Yang Wang +5
Graph Anomaly Detection (GAD) has recently become a hot research spot due to its practicability and theoretical value. Since GAD emphasizes the application and the rarity of anomal…
DropMessage: Unifying Random Dropping for Graph Neural Networks
Taoran Fang, Zhiqing Xiao, Chunping Wang +3
Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoot…
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
Jiarong Xu, Yizhou Sun, Xin Jiang +4
Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queri…
Learning Fair Representations via an Adversarial Framework
Rui Feng, Yang Yang, Yuehan Lyu +3
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approv…