Popularity prediction on social platforms with coupled graph neural networks
Predicting the popularity of online content on social platforms is an important task for both
researchers and practitioners. Previous methods mainly leverage demographics, temporal …
researchers and practitioners. Previous methods mainly leverage demographics, temporal …
Graph-based stock correlation and prediction for high-frequency trading systems
T Yin, C Liu, F Ding, Z Feng, B Yuan, N Zhang - Pattern Recognition, 2022 - Elsevier
In this paper, we have implemented a high-frequency quantitative system that can obtain
stable returns for the Chinese A-share market, which has been running for more than 3 …
stable returns for the Chinese A-share market, which has been running for more than 3 …
Hawkes Models and Their Applications
PJ Laub, Y Lee, PK Pollett… - Annual Review of Statistics …, 2024 - annualreviews.org
The Hawkes process is a model for counting the number of arrivals to a system that exhibits
the self-exciting property—that one arrival creates a heightened chance of further arrivals in …
the self-exciting property—that one arrival creates a heightened chance of further arrivals in …
Neural point process for learning spatiotemporal event dynamics
Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point
processes enhance the expressivity of point process models with deep neural networks …
processes enhance the expressivity of point process models with deep neural networks …
Automatic integration for spatiotemporal neural point processes
Learning continuous-time point processes is essential to many discrete event forecasting
tasks. However, integration poses a major challenge, particularly for spatiotemporal point …
tasks. However, integration poses a major challenge, particularly for spatiotemporal point …
Learning neural point processes with latent graphs
Neural point processes (NPPs) employ neural networks to capture complicated dynamics of
asynchronous event sequences. Existing NPPs feed all history events into neural networks …
asynchronous event sequences. Existing NPPs feed all history events into neural networks …
[PDF][PDF] Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph.
Relation discovery for multi-dimensional temporal point processes (MTPP) has received
increasing interest for its importance in prediction and interpretability of the underlying …
increasing interest for its importance in prediction and interpretability of the underlying …
STHKT: Spatiotemporal Knowledge Tracing with Topological Hawkes Process
Abstract Knowledge Tracing (KT) is a method that seeks to forecast students' future
performance based on their historical interactions with intelligent tutoring systems. Various …
performance based on their historical interactions with intelligent tutoring systems. Various …
THPs: Topological Hawkes processes for learning causal structure on event sequences
Learning causal structure among event types on multitype event sequences is an important
but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly …
but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly …
Enhancing event sequence modeling with contrastive relational inference
Neural temporal point processes (TPPs) have shown promise for modeling continuous-time
event sequences. However, capturing the interactions between events is challenging yet …
event sequences. However, capturing the interactions between events is challenging yet …