b'Carl Yang'

research

∙ 05/06/2023

Transformer-Based Hierarchical Clustering for Brain Network Analysis

Brain networks, graphical models such as those constructed from MRI, hav...

34 Wei Dai, et al. ∙

research

∙ 03/29/2023

When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective!

Recently, graph pre-training has attracted wide research attention, whic...

0 Yuxuan Cao, et al. ∙

research

∙ 02/06/2023

MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields

Multimodal learning has attracted the interest of the machine learning c...

0 Jiaying Lu, et al. ∙

research

∙ 01/10/2023

Neighborhood-Regularized Self-Training for Learning with Few Labels

Training deep neural networks (DNNs) with limited supervision has been a...

12 Ran Xu, et al. ∙

research

∙ 12/23/2022

Graph Federated Learning with Hidden Representation Sharing

Learning on Graphs (LoG) is widely used in multi-client systems when eac...

2 Shuang Wu, et al. ∙

research

∙ 10/13/2022

Brain Network Transformer

Human brains are commonly modeled as networks of Regions of Interest (RO...

15 Xuan Kan, et al. ∙

research

∙ 10/10/2022

Towards Training Graph Neural Networks with Node-Level Differential Privacy

Graph Neural Networks (GNNs) have achieved great success in mining graph...

0 Qiuchen Zhang, et al. ∙

research

∙ 06/30/2022

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Human brains lie at the core of complex neurobiological systems, where t...

13 Hejie Cui, et al. ∙

research

∙ 06/24/2022

Deep Generation of Heterogeneous Networks

Heterogeneous graphs are ubiquitous data structures that can inherently ...

0 Chen Ling, et al. ∙

research

∙ 06/09/2022

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

Brain networks characterize complex connectivities among brain regions a...

13 Yi Yang, et al. ∙

research

∙ 06/06/2022

A Bird's-Eye Tutorial of Graph Attention Architectures

Graph Neural Networks (GNNs) have shown tremendous strides in performanc...

0 Kaustubh D. Dhole, et al. ∙

research

∙ 05/25/2022

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

Functional magnetic resonance imaging (fMRI) is one of the most common i...

17 Xuan Kan, et al. ∙

research

∙ 05/08/2022

Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

Graph neural networks (GNNs) have been widely used in modeling graph str...

0 Yuanxin Zhuang, et al. ∙

research

∙ 03/17/2022

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well ...

7 Kai Zhang, et al. ∙

research

∙ 03/17/2022

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

Mapping the connectome of the human brain using structural or functional...

0 Hejie Cui, et al. ∙

research

∙ 02/18/2022

Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

Graph neural networks (GNNs) have drawn significant research attention r...

6 Mingyue Tang, et al. ∙

research

∙ 02/15/2022

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

Social recommendation has shown promising improvements over traditional ...

0 Jamie Cui, et al. ∙

research

∙ 01/12/2022

How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

Graph neural networks (GNNs), as a group of powerful tools for represent...

16 Hejie Cui, et al. ∙

research

∙ 11/21/2021

Structure-Preserving Graph Kernel for Brain Network Classification

This paper presents a novel graph-based kernel learning approach for con...

0 Zhaomin Kong, et al. ∙

research

∙ 08/31/2021

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de fa...

5 Yanqiao Zhu, et al. ∙

research

∙ 07/23/2021

Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

Recent studies in neuroscience show great potential of functional brain ...

0 Xuan Kan, et al. ∙

research

∙ 07/11/2021

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

Interpretable brain network models for disease prediction are of great v...

25 Hejie Cui, et al. ∙

research

∙ 07/11/2021

Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration

Relation prediction among entities in images is an important step in sce...

0 Xuan Kan, et al. ∙

research

∙ 07/07/2021

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

Multimodal brain networks characterize complex connectivities among diff...

10 Yanqiao Zhu, et al. ∙

research

∙ 07/03/2021

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

Graph neural networks (GNNs) have been widely used in various graph-rela...

0 Hejie Cui, et al. ∙

research

∙ 06/25/2021

Subgraph Federated Learning with Missing Neighbor Generation

Graphs have been widely used in data mining and machine learning due to ...

9 Ke Zhang, et al. ∙

research

∙ 06/25/2021

Federated Graph Classification over Non-IID Graphs

Federated learning has emerged as an important paradigm for training mac...

0 Han Xie, et al. ∙

research

∙ 05/31/2021

Controllable Gradient Item Retrieval

In this paper, we identify and study an important problem of gradient it...

0 Haonan Wang, et al. ∙

research

∙ 05/07/2021

Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks

Graph representation learning has achieved great success in many areas, ...

12 Gongxu Luo, et al. ∙

research

∙ 03/27/2021

Multi-Facet Recommender Networks with Spherical Optimization

Implicit feedback is widely explored by modern recommender systems. Sinc...

0 Yanchao Tan, et al. ∙

research

∙ 09/11/2020

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

Graph neural networks (GNNs) have been shown with superior performance i...

36 Qi Zhu, et al. ∙

research

∙ 07/06/2020

GCN for HIN via Implicit Utilization of Attention and Meta-paths

Heterogeneous information network (HIN) embedding, aiming to map the str...

0 Di Jin, et al. ∙

research

∙ 06/07/2020

Unsupervised Differentiable Multi-aspect Network Embedding

Network embedding is an influential graph mining technique for represent...

2 Chanyoung Park, et al. ∙

research

∙ 05/01/2020

Secure Network Release with Link Privacy

Many data mining and analytical tasks rely on the abstraction of network...

0 Carl Yang, et al. ∙

research

∙ 04/01/2020

Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond

Since real-world objects and their interactions are often multi-modal an...

0 Carl Yang, et al. ∙

research

∙ 09/29/2019

I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

As online platforms are striving to get more users, a critical challenge...

0 Carl Yang, et al. ∙

research

∙ 09/29/2019

Query-Specific Knowledge Summarization with Entity Evolutionary Networks

Given a query, unlike traditional IR that finds relevant documents or en...

0 Carl Yang, et al. ∙

research

∙ 09/29/2019

Neural Embedding Propagation on Heterogeneous Networks

Classification is one of the most important problems in machine learning...

0 Carl Yang, et al. ∙

research

∙ 09/29/2019

Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

In this work, we propose to study the utility of different meta-graphs, ...

0 Carl Yang, et al. ∙

research

∙ 08/04/2019

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to ...

0 Carl Yang, et al. ∙

research

∙ 11/28/2018

Higher-Order Clustering in Heterogeneous Information Networks

As one type of complex networks widely-seen in real-world application, h...

0 Yu Shi, et al. ∙

research

∙ 04/18/2018

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

We implement exact triangle counting in graphs on the GPU using three di...

0 Leyuan Wang, et al. ∙

research

∙ 04/10/2018

Implementing Push-Pull Efficiently in GraphBLAS

We factor Beamer's push-pull, also known as direction-optimized breadth-...

0 Carl Yang, et al. ∙

research

∙ 03/22/2018

Design Principles for Sparse Matrix Multiplication on the GPU

We implement two novel algorithms for sparse-matrix dense-matrix multipl...

0 Carl Yang, et al. ∙

research

∙ 01/19/2018

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

Multi-view networks are ubiquitous in real-world applications. In order ...

0 Yu Shi, et al. ∙

research

∙ 06/18/2016

Mathematical Foundations of the GraphBLAS

The GraphBLAS standard (GraphBlas.org) is being developed to bring the p...

0 Jeremy Kepner, et al. ∙

Carl Yang

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