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Found 20 papers, 13 papers with code
Improving Temporal Link Prediction via Temporal Walk Matrix Projection
Temporal link prediction, aiming at predicting future interactions among entities based on historical interactions, is crucial for a series of real-world applications.
Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing
To tap into the rich information within population movement, based on the perspective that Regions Are Who walk them, we propose a large spatiotemporal model based on trajectories (RAW).
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
To capture the correlation between molecular graphs and geometries in the diffusion process, we develop a Diffusion Graph Transformer to parameterize the data prediction model that recovers the original data from noisy data.
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation.
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
Graph generative models have broad applications in biology, chemistry and social science.
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction
Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society.
Label-Enhanced Graph Neural Network for Semi-supervised Node Classification
In recent years, several methods have been designed to additionally utilize the labels at the input.
Ranked #19 on
Node Property Prediction
on ogbn-mag
Continuous-Time User Preference Modelling for Temporal Sets Prediction
Previous studies for temporal sets prediction mainly focus on the modelling of elements and implicitly represent each user's preference based on his/her interacted elements.
Collaborative Learning in General Graphs with Limited Memorization: Complexity, Learnability, and Reliability
The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives.
Heterogeneous Graph Representation Learning with Relation Awareness
Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #22 on
Node Property Prediction
on ogbn-mag
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #24 on
Node Property Prediction
on ogbn-mag
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.
Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States
Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value).
Multiway Attention Networks for Modeling Sentence Pairs
Modeling sentence pairs plays the vital role for judging the relationship between two sentences, such as paraphrase identification, natural language inference, and answer sentence selection.
Ranked #11 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
We build the answer extraction model with state-of-the-art neural networks for single passage reading comprehension, and propose an additional task of passage ranking to help answer extraction in multiple passages.
Entity Linking for Queries by Searching Wikipedia Sentences
The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query.
Feature Selection Based on Term Frequency and T-Test for Text Categorization
Much work has been done on feature selection.
