A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Wingnn: Dynamic graph neural networks with random gradient aggregation window
Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …
DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
It has been observed that POI recommendation is driven by both sequential and …
It has been observed that POI recommendation is driven by both sequential and …
Causality-based CTR prediction using graph neural networks
P Zhai, Y Yang, C Zhang - Information Processing & Management, 2023 - Elsevier
As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention
from both academia and industry. Recent studies have been reported to establish CTR …
from both academia and industry. Recent studies have been reported to establish CTR …
Learning graph ode for continuous-time sequential recommendation
Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …
behavior correlations, which are usually represented as the item purchasing sequences …
Towards integrated and fine-grained traffic forecasting: A spatio-temporal heterogeneous graph transformer approach
Fine-grained traffic forecasting is crucial for the management of urban transportation
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …
systems. Road segments and intersection turns, as vital elements of road networks, exhibit …
A diffusion model for poi recommendation
Next Point-of-Interest (POI) recommendation is a critical task in location-based services that
aim to provide personalized suggestions for the user's next destination. Previous works on …
aim to provide personalized suggestions for the user's next destination. Previous works on …
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as
the final-stage filter to rank items for a user. The key to addressing the CTR task is learning …
the final-stage filter to rank items for a user. The key to addressing the CTR task is learning …
Out-of-distribution generalized dynamic graph neural network with disentangled intervention and invariance promotion
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks
In e-commerce, fake reviewers frequently post fake reviews to mislead consumers into
making unwise shopping decisions, seriously affecting customers' benefits. Graph neural …
making unwise shopping decisions, seriously affecting customers' benefits. Graph neural …