2.4k citations · 3.4k across the 27 of their papers we have counts for
28 papers
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…
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
Yitong Li, Duoduo Liao, Jundong Li +1
When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated w…
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models explo…
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention i…
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…
Scalable Attack on Graph Data by Injecting Vicious Nodes
Jihong Wang, Minnan Luo, Fnu Suya +3
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph…
Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent repor…
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
Min Gao, Junwei Zhang, Junliang Yu +3
Recommender systems (RSs) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from an array of optio…
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…
Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Junliang Yu, Min Gao, Hongzhi Yin +3
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditi…
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…
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Yuxin Ma, Tiankai Xie, Jundong Li +1
Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and nu…
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…
A Survey of Learning Causality with Data: Problems and Methods
Ruocheng Guo, Lu Cheng, Jundong Li +2
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era o…
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
Jundong Li, Liang Wu, Huan Liu
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the pr…
Attributed Network Embedding for Learning in a Dynamic Environment
Jundong Li, Harsh Dani, Xia Hu +3
Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance v…