314 citations · 395 across the 15 of their papers we have counts for
15 papers
LPS-GNN : Deploying Graph Neural Networks on Graphs with 100-Billion Edges
Xu Cheng, Liang Yao, Feng He +6
Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with pred…
Can Language Models Discover Scaling Laws?
Haowei Lin, Haotian Ye, Wenzheng Feng +8
Discovering scaling laws for predicting model performance at scale is a fundamental and open-ended challenge, mostly reliant on slow, case specific human experimentation. To invest…
Learning Evolving Tools for Large Language Models
Guoxin Chen, Zhong Zhang, Xin Cong +5
Tool learning enables large language models (LLMs) to interact with external tools and APIs, greatly expanding the application scope of LLMs. However, due to the dynamic nature of…
Using Graph Neural Networks for Program Termination
Yoav Alon, Cristina David
Termination analyses investigate the termination behavior of programs, intending to detect nontermination, which is known to cause a variety of program bugs (e.g. hanging programs,…
Online Coordination: Methods and Comparative Case Studies of Coordinated Groups across Four Events in the United States
Lynnette Hui Xian Ng, Kathleen M. Carley
Coordinated groups of user accounts working together in online social media can be used to manipulate the online discourse and thus is an important area of study. In this study, we…
GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang +4
Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate s…
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
Jibing Gong, Yao Wan, Ye Liu +8
Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and…
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert, Alex Haig, Yinlam Chow +7
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., click…
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Qingsong Lv, Ming Ding, Qiang Liu +7
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understandi…
SCR: Training Graph Neural Networks with Consistency Regularization
Chenhui Zhang, Yufei He, Yukuo Cen +7
We present the SCR framework for enhancing the training of graph neural networks (GNNs) with consistency regularization. Regularization is a set of strategies used in Machine Learn…
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
Tianyu Zhang, Yuxiang Ren, Wenzheng Feng +2
Unsupervised graph representation learning is a non-trivial topic. The success of contrastive methods in the unsupervised representation learning on structured data inspires simila…
SolType: Refinement Types for Arithmetic Overflow in Solidity
Bryan Tan, Benjamin Mariano, Shuvendu K. Lahiri +2
As smart contracts gain adoption in financial transactions, it becomes increasingly important to ensure that they are free of bugs and security vulnerabilities. Of particular relev…
Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth
Georg Gottlob, Matthias Lanzinger, Cem Okulmus +1
Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient quer…
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
Shen Wang, Jibing Gong, Jinlong Wang +4
Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attra…
Graph Random Neural Network for Semi-Supervised Learning on Graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong +6
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer fro…