154 citations · 331 across the 23 of their papers we have counts for
24 papers
ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
Xiaobao Huang, Yihong Ma, Anjali Gurajapu +4
Reaction virtual screening and discovery are fundamental challenges in chemistry and materials science, where traditional graph neural networks (GNNs) struggle to model multi-react…
Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond
Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel +8
The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic a…
MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension
Khiem Le, Zhichun Guo, Kaiwen Dong +8
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understandi…
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often comp…
You do not have to train Graph Neural Networks at all on text-attributed graphs
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node clas…
Universal Link Predictor By In-Context Learning on Graphs
Kaiwen Dong, Haitao Mao, Zhichun Guo +1
Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods ba…
Node Duplication Improves Cold-start Link Prediction
Zhichun Guo, Tong Zhao, Yozen Liu +5
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show tha…
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle…
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
Taicheng Guo, Kehan Guo, Bozhao Nan +5
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and so…
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo +2
Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the…
Link Prediction with Non-Contrastive Learning
William Shiao, Zhichun Guo, Tong Zhao +3
A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Not…
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
Zhichun Guo, Chunhui Zhang, Yujie Fan +3
Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they lea…
Linkless Link Prediction via Relational Distillation
Zhichun Guo, William Shiao, Shichang Zhang +4
Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood da…
Flashlight: Scalable Link Prediction with Effective Decoders
Yiwei Wang, Bryan Hooi, Yozen Liu +3
Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring ne…
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Yijun Tian, Chuxu Zhang, Zhichun Guo +2
While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the…
Graph-based Molecular Representation Learning
Zhichun Guo, Kehan Guo, Bozhao Nan +8
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vector…
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborativ…
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the…
A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods
Zhihan Zhang, Wenhao Yu, Mengxia Yu +2
Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalitie…
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance
Zhichun Guo, Jun Tao, Siming Chen +2
Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks of…
Sentence-Permuted Paragraph Generation
Wenhao Yu, Chenguang Zhu, Tong Zhao +2
Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-…
Few-Shot Graph Learning for Molecular Property Prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu +4
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network m…
Action Sequence Augmentation for Early Graph-based Anomaly Detection
Tong Zhao, Bo Ni, Wenhao Yu +3
The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behav…
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
Bo Ni, Zhichun Guo, Jianing Li +1
Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this pa…