research
∙
09/11/2023
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
Hypergraphs can naturally model group-wise relations (e.g., a group of u...
share
research
∙
08/29/2023
Ensuring User-side Fairness in Dynamic Recommender Systems
User-side group fairness is crucial for modern recommender systems, as i...
research
∙
08/27/2023
Topological Augmentation for Class-Imbalanced Node Classification
Class imbalance is prevalent in real-world node classification tasks and...
research
∙
08/12/2023
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams
Graph or network data are widely studied in both data mining and visuali...
research
∙
07/10/2023
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
In graph machine learning, data collection, sharing, and analysis often ...
research
∙
06/13/2023
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
This paper studies speculative reasoning task on real-world knowledge gr...
research
∙
06/01/2023
Reconstructing Graph Diffusion History from a Single Snapshot
Diffusion on graphs is ubiquitous with numerous high-impact applications...
research
∙
05/29/2023
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
Multivariate time series (MTS) imputation is a widely studied problem in...
research
∙
05/22/2023
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
Probabilistic logical rule learning has shown great strength in logical ...
research
∙
02/23/2023
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
The political stance prediction for news articles has been widely studie...
research
∙
02/22/2023
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are th...
research
∙
01/19/2023
Concept Discovery for Fast Adapatation
The advances in deep learning have enabled machine learning methods to o...
research
∙
11/08/2022
GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network
Subteam replacement is defined as finding the optimal candidate set of p...
research
∙
10/12/2022
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
Graph Convolutional Network (GCN) has exhibited strong empirical perform...
research
∙
10/04/2022
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
We study high-probability regret bounds for adversarial K-armed bandits ...
research
∙
10/02/2022
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(N...
research
∙
09/27/2022
Retrieval Based Time Series Forecasting
Time series data appears in a variety of applications such as smart tran...
research
∙
08/15/2022
ARIEL: Adversarial Graph Contrastive Learning
Contrastive learning is an effective unsupervised method in graph repres...
research
∙
06/30/2022
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Directly motivated by security-related applications from the Homeland Se...
research
∙
06/19/2022
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Despite the success of the Sylvester equation empowered methods on vario...
research
∙
06/06/2022
Schema-Guided Event Graph Completion
We tackle a new task, event graph completion, which aims to predict miss...
research
∙
06/01/2022
CoNSoLe: Convex Neural Symbolic Learning
Learning the underlying equation from data is a fundamental problem in m...
research
∙
05/31/2022
COIN: Co-Cluster Infomax for Bipartite Graphs
Bipartite graphs are powerful data structures to model interactions betw...
research
∙
05/16/2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph...
research
∙
05/06/2022
Learning Optimal Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved tremendous success in a varie...
research
∙
05/05/2022
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Bundle recommendation is an emerging research direction in the recommend...
research
∙
04/21/2022
Detecting Topology Attacks against Graph Neural Networks
Graph neural networks (GNNs) have been widely used in many real applicat...
research
∙
03/09/2022
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influe...
research
∙
02/28/2022
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world...
research
∙
02/14/2022
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph repres...
research
∙
11/22/2021
Backdoor Attack through Frequency Domain
Backdoor attacks have been shown to be a serious threat against deep lea...
research
∙
10/28/2021
Graph Communal Contrastive Learning
Graph representation learning is crucial for many real-world application...
research
∙
10/16/2021
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in cl...
research
∙
10/08/2021
Fair Regression under Sample Selection Bias
Recent research on fair regression focused on developing new fairness no...
research
∙
10/01/2021
Unsupervised Belief Representation Learning in Polarized Networks with Information-Theoretic Variational Graph Auto-Encoders
This paper develops a novel unsupervised algorithm for belief representa...
research
∙
09/08/2021
Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs
Contrastive Learning (CL) is one of the most popular self-supervised lea...
research
∙
08/29/2021
Multiplex Graph Neural Network for Extractive Text Summarization
Extractive text summarization aims at extracting the most representative...
research
∙
05/24/2021
MultiFair: Multi-Group Fairness in Machine Learning
Algorithmic fairness is becoming increasingly important in data mining a...
research
∙
05/23/2021
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applic...
research
∙
05/19/2021
Graph Sanitation with Application to Node Classification
The past decades have witnessed the prosperity of graph mining, with a m...
research
∙
05/02/2021
Graph Vulnerability and Robustness: A Survey
The study of network robustness is a critical tool in the characterizati...
research
∙
02/15/2021
HDMI: High-order Deep Multiplex Infomax
Networks have been widely used to represent the relations between object...
research
∙
02/15/2021
Network of Tensor Time Series
Co-evolving time series appears in a multitude of applications such as e...
research
∙
01/26/2021
REFORM: Fast and Adaptive Solution for Subteam Replacement
In this paper, we propose the novel problem of Subteam Replacement: give...
research
∙
11/06/2020
KompaRe: A Knowledge Graph Comparative Reasoning System
Reasoning is a fundamental capability for harnessing valuable insight, k...
research
∙
10/10/2020
Fairness-aware Agnostic Federated Learning
Federated learning is an emerging framework that builds centralized mach...
research
∙
09/15/2020
Auditing the Sensitivity of Graph-based Ranking with Visual Analytics
Graph mining plays a pivotal role across a number of disciplines, and a ...
research
∙
10/20/2019
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
A recent trend of fair machine learning is to define fairness as causali...
research
∙
07/29/2019
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data
The rapid development of urbanization during the past decades has signif...
research
∙
05/14/2019