2.4k citations · 7.9k across the 182 of their papers we have counts for
71 papers · 1 filter
Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation
Yu Song, Zhigang Hua, Yan Xie +5
Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and observational constraints,…
Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates
Weiyi He, Yuping Lin, Jiliang Tang +1
Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scales, especially for large langua…
A Simple Plug-in for Improving Eviction-Based KV Cache Compression
Yuping Lin, Jiayuan Ding, Yue Xing +3
KV cache growth is a major bottleneck for long-context inference in large language models. Existing methods are often dominated by binary eviction or representation approximation,…
Local Message-Passing for Discrete Graph Generation
Jay Revolinsky, Harry Shomer, Jiliang Tang
Discrete graph generation has emerged as a powerful paradigm for modeling graph-structured data, yet state of the art models often rely on Graph Transformers or higher order archit…
Plain Transformers are Surprisingly Powerful Link Predictors
Quang Truong, Yu Song, Donald Loveland +4
Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the s…
SA-GDA: Spectral Augmentation for Graph Domain Adaptation
Jinhui Pang, Zixuan Wang, Jiliang Tang +2
Graph neural networks (GNNs) have achieved impressive impressions for graph-related tasks. However, most GNNs are primarily studied under the cases of signal domain with supervised…
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
Kai Guo, Zewen Liu, Zhikai Chen +4
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks. Recently, several LLMs-based pipelines have been developed t…
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
Zhikai Chen, Haitao Mao, Jingzhe Liu +8
Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unifie…
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
Bingheng Li, Linxin Yang, Yupeng Chen +8
Solving large-scale linear programming (LP) problems is an important task in various areas such as communication networks, power systems, finance and logistics. Recently, two disti…
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
Haoyu Han, Juanhui Li, Wei Huang +5
Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global…
Towards Understanding Link Predictor Generalizability Under Distribution Shifts
Jay Revolinsky, Harry Shomer, Jiliang Tang
State-of-the-art link prediction (LP) models demonstrate impressive benchmark results. However, popular benchmark datasets often assume that training, validation, and testing sampl…
Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models
Wenzhuo Tang, Haitao Mao, Danial Dervovic +4
Models for natural language and images benefit from data scaling behavior: the more data fed into the model, the better they perform. This 'better with more' phenomenon enables the…
Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
Kai Guo, Hongzhi Wen, Wei Jin +3
Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world sce…
Masked Graph Autoencoder with Non-discrete Bandwidths
Ziwen Zhao, Yuhua Li, Yixiong Zou +2
Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…
Mixture of Link Predictors on Graphs
Li Ma, Haoyu Han, Juanhui Li +4
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise m…
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Soo Yong Lee, Sunwoo Kim, Fanchen Bu +3
How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between…
Position: Graph Foundation Models are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang +6
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance the…
Towards Neural Scaling Laws on Graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen +3
Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural sca…
Superiority of Multi-Head Attention in In-Context Linear Regression
Yingqian Cui, Jie Ren, Pengfei He +2
We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks. While the existing literature predom…
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
Harry Shomer, Yao Ma, Juanhui Li +3
Knowledge graph completion (KGC) aims to predict unseen edges in knowledge graphs (KGs), resulting in the discovery of new facts. A new class of methods have been proposed to tackl…
LPFormer: An Adaptive Graph Transformer for Link Prediction
Harry Shomer, Yao Ma, Haitao Mao +3
Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristi…
Label-free Node Classification on Graphs with Large Language Models (LLMS)
Zhikai Chen, Haitao Mao, Hongzhi Wen +5
In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to…
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Geri Skenderi, Hang Li, Jiliang Tang +1
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-ba…
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Zhikai Chen, Haitao Mao, Hang Li +8
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily…