Search Results for author:
Found 113 papers, 61 papers with code
Privacy-Enhancing Paradigms within Federated Multi-Agent Systems
LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles.
Subgraph Federated Learning for Local Generalization
In contrast, our proposed method, FedLoG, effectively tackles this issue by mitigating local overfitting.
Node-level Contrastive Unlearning on Graph Neural Networks
Graph unlearning aims to remove a subset of graph entities (i. e. nodes and edges) from a graph neural network (GNN) trained on the graph.
Scalable Graph Condensation with Evolving Capabilities
However, real-world graph data continues to grow exponentially, resulting in a quadratic increase in the complexity of most graph algorithms as graph sizes expand.
End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework
Brain imaging analysis is fundamental in neuroscience, providing valuable insights into brain structure and function.
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis
Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness.
Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?
Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders.
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling
Recently proposed personalized subgraph FL methods have become the de-facto standard for training personalized Graph Neural Networks (GNNs) in a federated manner while dealing with the missing links across clients' subgraphs due to privacy restrictions.
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one.
Evaluating the Impact of Lab Test Results on Large Language Models Generated Differential Diagnoses from Clinical Case Vignettes
GPT-4 performed best, achieving 55% accuracy for Top 1 diagnoses and 60% for Top 10 with lab data, with lenient accuracy up to 80%.
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge.
Measuring Spiritual Values and Bias of Large Language Models
Large language models (LLMs) have become integral tool for users from various backgrounds.
Integrating Planning into Single-Turn Long-Form Text Generation
To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles.
Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation
Unlike traditional methods, which generate two responses before obtaining the preference label, RMBoost first generates one response and selects a preference label, followed by generating the second more (or less) preferred response conditioned on the pre-selected preference label and the first response.
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
Despite the rapid development of GC methods, particularly for node classification, a unified evaluation framework is still lacking to systematically compare different GC methods or clarify key design choices for improving their effectiveness.
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
In this work, we propose a data-centric framework, Biomedical Visual Instruction Tuning with Clinician Preference Alignment (BioMed-VITAL), that incorporates clinician preferences into both stages of generating and selecting instruction data for tuning biomedical multimodal foundation models.
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP.
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data.
GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
We also show that GuardAgent is able to define novel functions in adaption to emergent LLM agents and guard requests, which underscores its strong generalization capabilities.
From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR
Electronic Health Records (EHRs) contain rich patient information and are crucial for clinical research and practice.
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation
The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes.
PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking
Furthermore, PromptLink is a generic framework without reliance on additional prior knowledge, context, or training data, making it well-suited for concept linking across various types of data sources.
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain.
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes.
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources.
Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning
In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat.
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction.
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
In this paper, we first revisit spatiotemporal time series imputation from a causal perspective and show how to block the confounders via the frontdoor adjustment.
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining
Traditional Graph Neural Networks (GNNs), which are commonly used for modeling attributed graphs, need to be re-trained every time when applied to different graph tasks and datasets.
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
Are Synthetic Time-series Data Really not as Good as Real Data?
Time-series data presents limitations stemming from data quality issues, bias and vulnerabilities, and generalization problem.
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes.
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Machine unlearning aims to eliminate the influence of a subset of training samples (i. e., unlearning samples) from a trained model.
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
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.
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
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
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
To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical NLP tasks.
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks.
Helper Recommendation with seniority control in Online Health Community
Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support.
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset.
Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training
To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains.
R-Mixup: Riemannian Mixup for Biological Networks
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
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.
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Information extraction, e. g., attribute value extraction, has been extensively studied and formulated based only on text.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways.
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module
Additionally, we reveal the drawbacks of previous residual methods, such as the lack of node adaptability and severe loss of high-order neighborhood subgraph information, and propose a \textbf{Posterior-Sampling-based, Node-Adaptive Residual module (PSNR)}.
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
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.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
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
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.
Neighborhood-Regularized Self-Training for Learning with Few Labels
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
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.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
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.
Brain Network Transformer
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
The key idea is to decouple the feature projection and message passing via a DP PageRank algorithm which learns the structure information and uses the top-$K$ neighbors determined by the PageRank for feature aggregation.
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
A Bird's-Eye Tutorial of Graph Attention Architectures
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.
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
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.
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
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.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
Shift-Robust Node Classification via Graph Adversarial Clustering
Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.
Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction
Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning.
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
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.
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 representation learning on irregular data, have manifested superiority in various downstream tasks.
Structure-Preserving Graph Kernel for Brain Network Classification
This paper presents a novel graph-based kernel learning approach for connectome analysis.
Weakly Supervised Concept Map Generation through Task-Guided Graph Translation
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.
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
Consequently, it is more challenging to encode the key information sources--relevant contexts and entity types.
Ranked #1 on
Relation Extraction
on CDR
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
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.
Understanding Structural Vulnerability in Graph Convolutional Networks
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
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
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks.
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
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.
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-related problems such as node classification and graph classification, where superior performance is mainly established when natural node features are available.
Federated Graph Classification over Non-IID Graphs
Federated learning has emerged as an important paradigm for training machine learning models in different domains.
Subgraph Federated Learning with Missing Neighbor Generation
Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions.
Controllable Gradient Item Retrieval
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.
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
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.
Multi-Facet Recommender Networks with Spherical Optimization
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.
A Survey on Graph Structure Learning: Progress and Opportunities
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.
Secure Network Release with Link Privacy
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.
GCN for HIN via Implicit Utilization of Attention and Meta-paths
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
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.
Secure Deep Graph Generation with Link Differential Privacy
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
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.
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization
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.
Non-local Attention Learning on Large Heterogeneous Information Networks
In this way, it leverages both local and non-local information simultaneously.
Ranked #1 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
(Macro-F1 (60% training data) metric)
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Graph embedding has been intensively studied recently, due to the advance of various neural network models.
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
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.
Place Deduplication with Embeddings
Thanks to the advancing mobile location services, people nowadays can post about places to share visiting experience on-the-go.
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application
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.
Query-Specific Knowledge Summarization with Entity Evolutionary Networks
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.
Neural Embedding Propagation on Heterogeneous Networks
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.
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU
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
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.
Adversarial Attack and Defense on Graph Data: A Survey
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.
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
We therefore approach the problem of user-guided clustering in HINs with network motifs.
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
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.
Graph Clustering with Dynamic Embedding
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
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
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.
