21 citations · 63 across the 35 of their papers we have counts for
23 papers · 1 filter
On the Safety of Graph Representation Learning
Xiaoguang Guo, Zehong Wang, Ziming Li +5
Graph representation learning (GRL) has evolved from topology-only graph embeddings to task-specific supervised GNNs, and more recently to reusable representations and graph founda…
Controllable Graph Generation with Diffusion Models via Inference-Time Tree Search Guidance
Jiachi Zhao, Zehong Wang, Yamei Liao +2
Graph generation is a fundamental problem in graph learning with broad applications across Web-scale systems, knowledge graphs, and scientific domains such as drug and material dis…
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…
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…
Generative Graph Pattern Machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma +2
Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their…
LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection
Tianyi Ma, Yiyue Qian, Zehong Wang +3
As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants. These…
Beyond Message Passing: Neural Graph Pattern Machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downs…
Training MLPs on Graphs without Supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges…
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Zehong Wang, Zheyuan Zhang, Tianyi Ma +3
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and s…
GFT: Graph Foundation Model with Transferable Tree Vocabulary
Zehong Wang, Zheyuan Zhang, Nitesh V Chawla +2
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Gr…
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
Zheyuan Zhang, Zehong Wang, Shifu Hou +7
The opioid crisis has been one of the most critical society concerns in the United States. Although the medication assisted treatment (MAT) is recognized as the most effective trea…
SimMLP: Training MLPs on Graphs without Supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges…
Subgraph Pooling: Tackling Negative Transfer on Graphs
Zehong Wang, Zheyuan Zhang, Chuxu Zhang +1
Transfer learning aims to enhance performance on a target task by using knowledge from related tasks. However, when the source and target tasks are not closely aligned, it can lead…
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning
Mingxuan Ju, Yujie Fan, Chuxu Zhang +1
Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security sta…
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Graph Contrastive Learning with Cross-view Reconstruction
Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang +3
Among different existing graph self-supervised learning strategies, graph contrastive learning (GCL) has been one of the most prevalent approaches to this problem. Despite the rema…
Multi-objective Deep Data Generation with Correlated Property Control
Shiyu Wang, Xiaojie Guo, Xuanyang Lin +11
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular desig…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
Few-Shot Learning on Graphs
Chuxu Zhang, Kaize Ding, Jundong Li +4
Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representat…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion
Chuanbo Hu, Minglei Yin, Bin Liu +2
Illicit drug trafficking via social media sites such as Instagram has become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencie…