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Found 32 papers, 22 papers with code
Dynamic Graph Representation Learning for Passenger Behavior Prediction
We capture the temporal patterns from individual sequences and correlate the temporal behavior between the two sequences.
DyGKT: Dynamic Graph Learning for Knowledge Tracing
Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static problem, this work is motivated by three dynamical characteristics: 1) The scales of students answering records are constantly growing; 2) The semantics of time intervals between the records vary; 3) The relationships between students, questions and concepts are evolving.
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
Furthermore, CNES introduces a Temporal-Diverse Memory to generate long-term and short-term structure encoding for neighbors with different structural information.
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
To fill this gap, this paper presents RepeatMixer, which considers evolving patterns of first and high-order repeat behavior in the neighbor sampling strategy and temporal information learning.
Pretraining Language Models with Text-Attributed Heterogeneous Graphs
In many real-world scenarios (e. g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships, which can be abstracted as Text-Attributed Heterogeneous Graphs (TAHGs).
Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives.
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.
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
Our proposed framework provides a conditional feature extraction module first to extract the coarse yet effective spatiotemporal dependencies from conditional information as the global context prior.
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.
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i. e., the legitimate targets for rebalancing operations can vary over time.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
Automated Urban Planning aware Spatial Hierarchies and Human Instructions
While automated urban planners have been examined, they are constrained because of the following: 1) neglecting human requirements in urban planning; 2) omitting spatial hierarchies in urban planning, and 3) lacking numerous urban plan data samples.
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.
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.
Graph Neural Network
Multivariate Time Series Forecasting
+2
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.
Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search
Shared e-mobility services have been widely tested and piloted in cities across the globe, and already woven into the fabric of modern urban planning.
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning
Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation.
Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data
Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis.
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
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection
To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs.
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.
Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning
Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms.
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
Demand Prediction for Electric Vehicle Sharing
Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe.
Attentive Crowd Flow Machines
Traffic flow prediction is crucial for urban traffic management and public safety.
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.
