2.4k citations · 3.4k across the 27 of their papers we have counts for
15 papers · 1 filter
Contrastive Meta-Learning for Few-shot Node Classification
Song Wang, Zhen Tan, Huan Liu +1
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining ta…
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding +2
Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous at…
Contrastive Graph Few-Shot Learning
Chunhui Zhang, Hongfu Liu, Jundong Li +2
Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradati…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
Few-Shot Learning on Graphs
Chuxu Zhang, Kaize Ding, Jundong Li +4
Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representat…
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in mo…
Line Graph Neural Networks for Link Prediction
Lei Cai, Jundong Li, Jie Wang +1
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link predicti…
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li +3
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central an…
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data
Ruocheng Guo, Jundong Li, Huan Liu
Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for pr…
Deep Structured Cross-Modal Anomaly Detection
Yuening Li, Ninghao Liu, Jundong Li +2
Anomaly detection is a fundamental problem in data mining field with many real-world applications. A vast majority of existing anomaly detection methods predominately focused on da…
Feature Interaction-aware Graph Neural Networks
Kaize Ding, Yichuan Li, Jundong Li +2
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on…
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Yuening Li, Xiao Huang, Jundong Li +2
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical…
Online Newton Step Algorithm with Estimated Gradient
Binbin Liu, Jundong Li, Yunquan Song +3
Online learning with limited information feedback (bandit) tries to solve the problem where an online learner receives partial feedback information from the environment in the cour…
Challenges of Feature Selection for Big Data Analytics
Jundong Li, Huan Liu
We are surrounded by huge amounts of large-scale high dimensional data. It is desirable to reduce the dimensionality of data for many learning tasks due to the curse of dimensional…
Feature Selection: A Data Perspective
Jundong Li, Kewei Cheng, Suhang Wang +4
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and…