Learning to sample and aggregate: Few-shot reasoning over temporal knowledge graphs
In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …
Coupled graph ode for learning interacting system dynamics
Many real-world systems such as social networks and moving planets are dynamic in
nature, where a set of coupled objects are connected via the interaction graph and exhibit …
nature, where a set of coupled objects are connected via the interaction graph and exhibit …
Multilingual knowledge graph completion with self-supervised adaptive graph alignment
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
complete. Due to labor-intensive human labeling, this phenomenon deteriorates when …
Generalizing graph ode for learning complex system dynamics across environments
Learning multi-agent system dynamics have been extensively studied for various real-world
applications, such as molecular dynamics in biology, multi-body system prediction in …
applications, such as molecular dynamics in biology, multi-body system prediction in …
A Survey of Information Dissemination Model, Datasets, and Insight
Y Liu, P Zhang, L Shi, J Gong - Mathematics, 2023 - mdpi.com
Information dissemination refers to how information spreads among users on social
networks. With the widespread application of mobile communication and internet …
networks. With the widespread application of mobile communication and internet …
Metahkg: Meta hyperbolic learning for few-shot temporal reasoning
This paper investigates the few-shot temporal reasoning capability within the hyperbolic
space. The goal is to forecast future events for newly emerging entities within temporal …
space. The goal is to forecast future events for newly emerging entities within temporal …
A survey on graph neural network acceleration: Algorithms, systems, and customized hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …
Dissecting cross-layer dependency inference on multi-layered inter-dependent networks
Multi-layered inter-dependent networks have emerged in a wealth of high-impact application
domains. Cross-layer dependency inference, which aims to predict the dependencies …
domains. Cross-layer dependency inference, which aims to predict the dependencies …
RETE: retrieval-enhanced temporal event forecasting on unified query product evolutionary graph
With the increasing demands on e-commerce platforms, numerous user action history is
emerging. Those enriched action records are vital to understand users' interests and intents …
emerging. Those enriched action records are vital to understand users' interests and intents …
Human mobility modeling during the COVID-19 pandemic via deep graph diffusion infomax
Abstract Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions,
have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of …
have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of …