Jundong Li

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2022 · arXiv (Cornell University)

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 for this task, there has been no standard comprehensive …

FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

2022 · arXiv (Cornell University)

Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adaptations to …

Label Informed Attributed Network Embedding

2017

Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that original network topological structure and node attribute proximity can be preserved in the vectors. These learned representations have been …

Enhancing Social Recommendation With Adversarial Graph Convolutional Networks

2020 · IEEE Transactions on Knowledge and Data Engineering

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According …