Search Results for author:
Found 41 papers, 10 papers with code
EventKE: Event-Enhanced Knowledge Graph Embedding
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
GENE: Global Event Network Embedding
Current methods for event representation ignore related events in a corpus-level global context.
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.
Learning Optimal Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.
Detecting Topology Attacks against Graph Neural Networks
In this work, we study the victim node detection problem under topology attacks against GNNs.
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed for numerous issues, such as political agendas and manipulating financial markets.
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Data Augmentation for Deep Graph Learning: A Survey
To counter the data noise and data scarcity issues in deep graph learning (DGL), increasing graph data augmentation research has been conducted lately.
Adversarial Graph Contrastive Learning with Information Regularization
Contrastive learning is an effective unsupervised method in graph representation learning.
Backdoor Attack through Frequency Domain
We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.
Graph Communal Contrastive Learning
We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar.
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
Fair Regression under Sample Selection Bias
However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs
To address these challenges, we propose a novel Graph Multi-View Prototypical (Graph-MVP) framework to extract node embeddings on multiplex graphs.
Multiplex Graph Neural Network for Extractive Text Summarization
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.
Event Time Extraction and Propagation via Graph Attention Networks
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
MultiFair: Multi-Group Fairness in Machine Learning
Algorithmic fairness is becoming increasingly important in data mining and machine learning, and one of the most fundamental notions is group fairness.
Hypergraph Pre-training with Graph Neural Networks
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
Graph Sanitation with Application to Node Classification
Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
HDMI: High-order Deep Multiplex Infomax
To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way.
Network of Tensor Time Series
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering
Collaborative filtering has been widely used in recommender systems.
KompaRe: A Knowledge Graph Comparative Reasoning System
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
Fairness-aware Agnostic Federated Learning
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
Evaluating Graph Vulnerability and Robustness using TIGER
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
Robust Principal Component Analysis with Adaptive Neighbors
More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i. e., the extreme noised ones are eliminated for the global robustness.
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions.
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
Panther: Fast Top-k Similarity Search in Large Networks
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
The Child is Father of the Man: Foresee the Success at the Early Stage
Understanding the dynamic mechanisms that drive the high-impact scientific work (e. g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources.
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.
Graph-based Anomaly Detection and Description: A Survey
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.
Social and Information Networks Cryptography and Security
Want a Good Answer? Ask a Good Question First!
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.
GenDeR: A Generic Diversified Ranking Algorithm
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.
