Online health communities (OHCs) are forums where patients with similar
...
Recent neuroimaging studies have highlighted the importance of
network-c...
Scientific document classification is a critical task for a wide range o...
Healthcare knowledge graphs (HKGs) have emerged as a promising tool for
...
Model pre-training on large text corpora has been demonstrated effective...
Biological networks are commonly used in biomedical and healthcare domai...
Graph Anomaly Detection (GAD) is a technique used to identify abnormal n...
Information extraction, e.g., attribute value extraction, has been
exten...
Large language models (LLMs) have significantly advanced the field of na...
Brain networks, graphical models such as those constructed from MRI, hav...
Recently, graph pre-training has attracted wide research attention, whic...
Multimodal learning has attracted the interest of the machine learning
c...
Training deep neural networks (DNNs) with limited supervision has been a...
Learning on Graphs (LoG) is widely used in multi-client systems when eac...
Human brains are commonly modeled as networks of Regions of Interest (RO...
Graph Neural Networks (GNNs) have achieved great success in mining
graph...
Human brains lie at the core of complex neurobiological systems, where t...
Heterogeneous graphs are ubiquitous data structures that can inherently
...
Brain networks characterize complex connectivities among brain regions a...
Graph Neural Networks (GNNs) have shown tremendous strides in performanc...
Functional magnetic resonance imaging (fMRI) is one of the most common
i...
Graph neural networks (GNNs) have been widely used in modeling graph
str...
Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well
...
Mapping the connectome of the human brain using structural or functional...
Graph neural networks (GNNs) have drawn significant research attention
r...
Social recommendation has shown promising improvements over traditional
...
Graph neural networks (GNNs), as a group of powerful tools for represent...
This paper presents a novel graph-based kernel learning approach for
con...
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de fa...
Recent studies in neuroscience show great potential of functional brain
...
Interpretable brain network models for disease prediction are of great v...
Relation prediction among entities in images is an important step in sce...
Multimodal brain networks characterize complex connectivities among diff...
Graph neural networks (GNNs) have been widely used in various graph-rela...
Graphs have been widely used in data mining and machine learning due to ...
Federated learning has emerged as an important paradigm for training mac...
In this paper, we identify and study an important problem of gradient it...
Graph representation learning has achieved great success in many areas,
...
Implicit feedback is widely explored by modern recommender systems. Sinc...
Graph neural networks (GNNs) have been shown with superior performance i...
Heterogeneous information network (HIN) embedding, aiming to map the
str...
Network embedding is an influential graph mining technique for represent...
Many data mining and analytical tasks rely on the abstraction of network...
Since real-world objects and their interactions are often multi-modal an...
As online platforms are striving to get more users, a critical challenge...
Given a query, unlike traditional IR that finds relevant documents or
en...
Classification is one of the most important problems in machine learning...
In this work, we propose to study the utility of different meta-graphs, ...
High-performance implementations of graph algorithms are challenging to
...
As one type of complex networks widely-seen in real-world application,
h...