Search Results for author: Lifang He

Found 44 papers, 15 papers with code

Multiplex Graph Networks for Multimodal Brain Network Analysis

1 code implementation31 Jul 2021 Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.

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

no code implementations7 Jul 2021 Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.

Contrastive Learning

Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

no code implementations21 Jun 2021 Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He

The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.

Contrastive Multi-Modal Clustering

no code implementations21 Jun 2021 Jie Xu, Huayi Tang, Yazhou Ren, Xiaofeng Zhu, Lifang He

(2) A feature contrastive module is proposed to learn common high-level semantic features from different modalities.

Contrastive Learning

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Federated Multi-View Learning for Private Medical Data Integration and Analysis

no code implementations4 May 2021 Sicong Che, Hao Peng, Lichao Sun, Yong Chen, Lifang He

This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).

Federated Learning MULTI-VIEW LEARNING

Non-Linear Fusion for Self-Paced Multi-View Clustering

no code implementations19 Apr 2021 Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

In NSMVC, we directly assign different exponents to different views according to their qualities.

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Event Detection

FedMood: Federated Learning on Mobile Health Data for Mood Detection

1 code implementation6 Feb 2021 Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, Lifang He

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.

Depression Detection Federated Learning +1

Heterogeneous Similarity Graph Neural Network on Electronic Health Records

no code implementations17 Jan 2021 Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu

EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.

CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

no code implementations10 Dec 2020 Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan

In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process.

Graph Classification Graph Representation Learning

A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods

no code implementations6 Nov 2020 Zhaoming Kong, Xiaowei Yang, Lifang He

Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance.

Image Denoising

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

1 code implementation26 Sep 2020 Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.

Graph Attention Text Generation

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

no code implementations12 Aug 2020 Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.

Adversarial Directed Graph Embedding

1 code implementation9 Aug 2020 Shijie Zhu, JianXin Li, Hao Peng, Senzhang Wang, Lifang He

To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.

Graph Embedding Graph Mining

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

no code implementations9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

Classification General Classification +2

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, Bo Li

Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph.

Adversarial Attack Image Classification +1

Boosted Sparse and Low-Rank Tensor Regression

2 code implementations NeurIPS 2018 Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.

A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.

Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

no code implementations11 Sep 2018 Jian-Guo Zhang, Ji Wang, Lifang He, Zhao Li, Philip S. Yu

Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.

Anomaly Detection

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

no code implementations11 Sep 2018 Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.

Network Embedding Tensor Decomposition

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

no code implementations19 Jun 2018 Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow

Network analysis of human brain connectivity is critically important for understanding brain function and disease states.

Graph Embedding

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

1 code implementation22 May 2018 Xi Sheryl Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.

Error-Robust Multi-View Clustering

no code implementations1 Jan 2018 Mehrnaz Najafi, Lifang He, Philip S. Yu

Various types of errors behave differently and inconsistently in each view.

Contaminant Removal for Android Malware Detection Systems

no code implementations7 Nov 2017 Lichao Sun, Xiaokai Wei, Jiawei Zhang, Lifang He, Philip S. Yu, Witawas Srisa-an

The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy

Cryptography and Security

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

no code implementations12 Sep 2017 Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin

Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.

Graph Embedding Graph Learning +2

Kernelized Support Tensor Machines

no code implementations ICML 2017 Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.

Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

no code implementations CVPR 2017 Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.

Classification General Classification

Learning from Multi-View Multi-Way Data via Structural Factorization Machines

no code implementations10 Apr 2017 Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu

Real-world relations among entities can often be observed and determined by different perspectives/views.

Online Multi-view Clustering with Incomplete Views

no code implementations2 Nov 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Philip S. Yu

We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement.

Online Unsupervised Multi-view Feature Selection

no code implementations27 Sep 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu

Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?

Feature Selection Sparse Learning

Multi-Source Multi-View Clustering via Discrepancy Penalty

no code implementations14 Apr 2016 Weixiang Shao, Jiawei Zhang, Lifang He, Philip S. Yu

In many real-world applications, information can be gathered from multiple sources, while each source can contain multiple views, which are more cohesive for learning.

DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

no code implementations31 Jul 2014 Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang

The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.

Classification General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.