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 …
Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field
Consumer loans, ie, loans to finance consumers to buy certain types of expenditures, is
increasingly popular in e-commerce platform. Different from traditional loans with mortgage …
increasingly popular in e-commerce platform. Different from traditional loans with mortgage …
Meta-path guided graph attention network for explainable herb recommendation
Y **, W Ji, Y Shi, X Wang, X Yang - Health Information Science and …, 2023 - Springer
Abstract Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by
Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in …
Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in …
Knowledge graph confidence-aware embedding for recommendation
C Huang, F Yu, Z Wan, F Li, H Ji, Y Li - Neural Networks, 2024 - Elsevier
Abstract Knowledge graphs (KG) are vital for extracting and storing knowledge from large
datasets. Current research favors knowledge graph-based recommendation methods, but …
datasets. Current research favors knowledge graph-based recommendation methods, but …
Learning tree structures from leaves for particle decay reconstruction
In this work, we present a neural approach to reconstructing rooted tree graphs describing
hierarchical interactions, using a novel representation we term the lowest common ancestor …
hierarchical interactions, using a novel representation we term the lowest common ancestor …
Information filtering and interpolating for semi-supervised graph domain adaptation
Graph domain adaptation, which falls under the umbrella of graph transfer learning, involves
transferring knowledge from a labeled source graph to improve prediction accuracy on an …
transferring knowledge from a labeled source graph to improve prediction accuracy on an …
Heterogeneous graph neural network with multi-view representation learning
In recent years, graph neural networks (GNNs)-based methods have been widely adopted
for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich …
for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich …
Deep adaptive graph clustering via von Mises-Fisher distributions
Graph clustering has been a hot research topic and is widely used in many fields, such as
community detection in social networks. Lots of works combining auto-encoder and graph …
community detection in social networks. Lots of works combining auto-encoder and graph …
Semantic-fused multi-granularity cross-city traffic prediction
Accurate traffic prediction is essential for effective urban management and the improvement
of transportation efficiency. Recently, data-driven traffic prediction methods have been …
of transportation efficiency. Recently, data-driven traffic prediction methods have been …
A dual-channel semi-supervised learning framework on graphs via knowledge transfer and meta-learning
This article studies the problem of semi-supervised learning on graphs, which aims to
incorporate ubiquitous unlabeled knowledge (eg, graph topology, node attributes) with few …
incorporate ubiquitous unlabeled knowledge (eg, graph topology, node attributes) with few …