Search Results for author: Carl Yang

Found 80 papers, 46 papers with code

Are Synthetic Time-series Data Really not as Good as Real Data?

no code implementations1 Feb 2024 Fanzhe Fu, Junru Chen, Jing Zhang, Carl Yang, Lvbin Ma, Yang Yang

Time-series data presents limitations stemming from data quality issues, bias and vulnerabilities, and generalization problem.

Representation Learning Time Series

Contrastive Unlearning: A Contrastive Approach to Machine Unlearning

no code implementations19 Jan 2024 Hong kyu Lee, Qiuchen Zhang, Carl Yang, Jian Lou, Li Xiong

Machine unlearning aims to eliminate the influence of a subset of training samples (i. e., unlearning samples) from a trained model.

Machine Unlearning Representation Learning

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

1 code implementation13 Jan 2024 Wenqi Shi, ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May D. Wang

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving.

Code Generation Few-Shot Learning +1

Deep Efficient Private Neighbor Generation for Subgraph Federated Learning

no code implementations9 Jan 2024 Ke Zhang, Lichao Sun, Bolin Ding, Siu Ming Yiu, Carl Yang

Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications.

Federated Learning Graph Mining

Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

1 code implementation1 Jan 2024 Guangji Bai, Zheng Chai, Chen Ling, Shiyu Wang, Jiaying Lu, Nan Zhang, Tingwei Shi, Ziyang Yu, Mengdan Zhu, Yifei Zhang, Carl Yang, Yue Cheng, Liang Zhao

We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.

Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks

1 code implementation NeurIPS 2023 Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, Yang Yang

The proposed pre-training pipeline is called the data-active graph pre-training (APT) framework, and is composed of a graph selector and a pre-training model.

Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models

1 code implementation1 Nov 2023 ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce Ho, Carl Yang

Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts.

Clinical Knowledge Knowledge Graphs +1

Helper Recommendation with seniority control in Online Health Community

no code implementations6 Sep 2023 Junruo Gao, Chen Ling, Carl Yang, Liang Zhao

Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support.

Recommendation Systems

Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis

1 code implementation5 Sep 2023 Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui, Ying Guo, Carl Yang

However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.

Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training

1 code implementation12 Jun 2023 ran Xu, Yue Yu, Joyce C. Ho, Carl Yang

To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only.

Document Classification Retrieval

R-Mixup: Riemannian Mixup for Biological Networks

no code implementations5 Jun 2023 Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.

Data Augmentation

GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

1 code implementation2 Jun 2023 Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets, Pan Li

Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains.

Fraud Detection Graph Anomaly Detection +1

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction

no code implementations1 Jun 2023 Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li

Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.

Attribute Attribute Value Extraction

PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis

1 code implementation20 May 2023 Yi Yang, Hejie Cui, Carl Yang

The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways.

Transfer Learning Unsupervised Pre-training

Deep Graph Neural Networks via Flexible Subgraph Aggregation

no code implementations9 May 2023 Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Di Jin, Carl Yang, Rui Zhang

Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution.

Transformer-Based Hierarchical Clustering for Brain Network Analysis

1 code implementation6 May 2023 Wei Dai, Hejie Cui, Xuan Kan, Ying Guo, Sanne van Rooij, Carl Yang

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.

Clustering

HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

no code implementations12 Apr 2023 Jiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, Carl Yang

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system.

Decision Making Knowledge Graphs

When to Pre-Train Graph Neural Networks? From Data Generation Perspective!

1 code implementation29 Mar 2023 Yuxuan Cao, Jiarong Xu, Carl Yang, Jiaan Wang, Yunchao Zhang, Chunping Wang, Lei Chen, Yang Yang

All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.

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

1 code implementation6 Feb 2023 Jiaying Lu, Yongchen Qian, Shifan Zhao, Yuanzhe Xi, Carl Yang

Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications.

Classification

Neighborhood-Regularized Self-Training for Learning with Few Labels

1 code implementation10 Jan 2023 ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, Carl Yang

Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.

Graph Federated Learning with Hidden Representation Sharing

no code implementations23 Dec 2022 Shuang Wu, Mingxuan Zhang, Yuantong Li, Carl Yang, Pan Li

On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients.

Federated Learning

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

1 code implementation1 Nov 2022 Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.

Time Series Time Series Analysis

Brain Network Transformer

2 code implementations13 Oct 2022 Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.

Clustering

Towards Training Graph Neural Networks with Node-Level Differential Privacy

no code implementations10 Oct 2022 Qiuchen Zhang, Jing Ma, Jian Lou, Carl Yang, Li Xiong

Furthermore, we analyze the privacy degradation caused by the sampling process dependent on the differentially private PageRank results during model training and propose a differentially private GNN (DPGNN) algorithm to further protect node features and achieve rigorous node-level differential privacy.

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

1 code implementation30 Jun 2022 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.

Disease Prediction

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

1 code implementation9 Jun 2022 Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang

Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.

Meta-Learning

A Bird's-Eye Tutorial of Graph Attention Architectures

no code implementations6 Jun 2022 Kaustubh D. Dhole, Carl Yang

Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems.

Graph Attention Graph Mining +2

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

1 code implementation25 May 2022 Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang

In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.

Time Series Time Series Analysis

Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

no code implementations8 May 2022 Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, Lichao Sun

Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications.

Generative Adversarial Network Graph Classification +3

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

1 code implementation17 Mar 2022 Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.

Entity Embeddings Federated Learning +4

Shift-Robust Node Classification via Graph Adversarial Clustering

no code implementations7 Mar 2022 Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han

Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.

Classification Clustering +2

Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

1 code implementation ICLR 2022 Mingyue Tang, Carl Yang, Pan Li

Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning.

Graph Mining Graph Reconstruction

Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation

no code implementations NeurIPS 2021 Jamie Cui, Chaochao Chen, Lingjuan Lyu, Carl Yang, Li Wang

As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.

Information Retrieval Retrieval

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

1 code implementation12 Jan 2022 Hejie Cui, Jiaying Lu, Yao Ge, Carl Yang

Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks.

Representation Learning Retrieval

Weakly Supervised Concept Map Generation through Task-Guided Graph Translation

1 code implementation8 Oct 2021 Jiaying Lu, Xiangjue Dong, Carl Yang

Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts.

Document Classification Translation

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

no code implementations31 Aug 2021 Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.

Contrastive Learning

Understanding Structural Vulnerability in Graph Convolutional Networks

1 code implementation13 Aug 2021 Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, Carl Yang

In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i. e., the weighted mean) of GCNs.

Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

no code implementations23 Jul 2021 Xuan Kan, Hejie Cui, Ying Guo, Carl Yang

Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.

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

1 code implementation11 Jul 2021 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.

Disease Prediction

Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration

1 code implementation11 Jul 2021 Xuan Kan, Hejie Cui, Carl Yang

Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks.

Graph Generation Graph Mining +2

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

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

2 code implementations3 Jul 2021 Hejie Cui, Zijie Lu, Pan Li, Carl Yang

Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are available.

Graph Classification Node Classification

Subgraph Federated Learning with Missing Neighbor Generation

1 code implementation NeurIPS 2021 Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu

Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions.

Federated Learning Graph Mining

Federated Graph Classification over Non-IID Graphs

1 code implementation NeurIPS 2021 Han Xie, Jing Ma, Li Xiong, Carl Yang

Federated learning has emerged as an important paradigm for training machine learning models in different domains.

Clustering Dynamic Time Warping +4

Controllable Gradient Item Retrieval

1 code implementation31 May 2021 Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He

A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.

Attribute Disentanglement +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

Multi-Facet Recommender Networks with Spherical Optimization

1 code implementation27 Mar 2021 Yanchao Tan, Carl Yang, Xiangyu Wei, Yun Ma, Xiaolin Zheng

Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation.

Metric Learning Recommendation Systems +1

A Survey on Graph Structure Learning: Progress and Opportunities

no code implementations4 Mar 2021 Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu

Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.

Graph structure learning

Secure Network Release with Link Privacy

no code implementations28 Sep 2020 Carl Yang, Haonan Wang, Ke Zhang, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation

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

1 code implementation NeurIPS 2021 Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.

Knowledge Graphs Transfer Learning

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

no code implementations6 Jul 2020 Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han

Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.

Unsupervised Differentiable Multi-aspect Network Embedding

1 code implementation7 Jun 2020 Chanyoung Park, Carl Yang, Qi Zhu, Donghyun Kim, Hwanjo Yu, Jiawei Han

To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.

Clustering Graph Clustering +2

Secure Deep Graph Generation with Link Differential Privacy

1 code implementation1 May 2020 Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation Link Prediction

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark

1 code implementation1 Apr 2020 Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, Jiawei Han

Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.

Attribute Network Embedding

cube2net: Efficient Query-Specific Network Construction with Data Cube Organization

no code implementations18 Jan 2020 Carl Yang, Mengxiong Liu, Frank He, Jian Peng, Jiawei Han

With extensive experiments of two classic network mining tasks on different real-world large datasets, we show that our proposed cube2net pipeline is general, and much more effective and efficient in query-specific network construction, compared with other methods without the leverage of data cube or reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

no code implementations4 Nov 2019 Carl Yang, Jieyu Zhang, Haonan Wang, Sha Li, Myungwan Kim, Matt Walker, Yiou Xiao, Jiawei Han

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.

Relation

Place Deduplication with Embeddings

no code implementations29 Sep 2019 Carl Yang, Do Huy Hoang, Tomas Mikolov, Jiawei Han

Thanks to the advancing mobile location services, people nowadays can post about places to share visiting experience on-the-go.

Query-Specific Knowledge Summarization with Entity Evolutionary Networks

no code implementations29 Sep 2019 Carl Yang, Lingrui Gan, Zongyi Wang, Jiaming Shen, Jinfeng Xiao, Jiawei Han

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization.

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

no code implementations29 Sep 2019 Carl Yang, Xiaolin Shi, Jie Luo, Jiawei Han

Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types.

Clustering

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

no code implementations29 Sep 2019 Carl Yang, Yichen Feng, Pan Li, Yu Shi, Jiawei Han

In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner.

Neural Embedding Propagation on Heterogeneous Networks

1 code implementation29 Sep 2019 Carl Yang, Jieyu Zhang, Jiawei Han

While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns.

Network Embedding

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

1 code implementation4 Aug 2019 Carl Yang, Aydin Buluc, John D. Owens

In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks.

Distributed, Parallel, and Cluster Computing Mathematical Software

Time-Series Event Prediction with Evolutionary State Graph

3 code implementations10 May 2019 Wenjie Hu, Yang Yang, Ziqiang Cheng, Carl Yang, Xiang Ren

In this paper, we present evolutionary state graph, a dynamic graph structure designed to systematically represent the evolving relations (edges) among states (nodes) along time.

Time Series Time Series Classification +1

Adversarial Attack and Defense on Graph Data: A Survey

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

Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.

Adversarial Attack Image Classification +1

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

1 code implementation19 Jan 2018 Yu Shi, Fangqiu Han, Xinwei He, Xinran He, Carl Yang, Jie Luo, Jiawei Han

With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.

Network Embedding

Graph Clustering with Dynamic Embedding

1 code implementation21 Dec 2017 Carl Yang, Mengxiong Liu, Zongyi Wang, Liyuan Liu, Jiawei Han

Unlike most existing embedding methods that are task-agnostic, we simultaneously solve for the underlying node representations and the optimal clustering assignments in an end-to-end manner.

Social and Information Networks Physics and Society

CONE: Community Oriented Network Embedding

no code implementations5 Sep 2017 Carl Yang, Hanqing Lu, Kevin Chen-Chuan Chang

It is usually modeled as an unsupervised clustering problem on graphs, based on heuristic assumptions about community characteristics, such as edge density and node homogeneity.

Social and Information Networks Physics and Society

Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

no code implementations1 Aug 2017 Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, J. Han profile

In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.

Collaborative Filtering Recommendation Systems

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