Bayesian Optimization of Functions over Node Subsets in Graphs

1 code implementation • 24 May 2024 • Huidong Liang, Xingchen Wan, Xiaowen Dong

We address the problem of optimizing over functions defined on node subsets in a graph.

ByteCover3: Accurate Cover Song Identification on Short Queries

no code implementations • 21 Mar 2023 • Xingjian Du, Zijie Wang, Xia Liang, Huidong Liang, Bilei Zhu, Zejun Ma

Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI.

Cover song identification Retrieval

Graph Contrastive Learning with Implicit Augmentations

1 code implementation • 7 Nov 2022 • Huidong Liang, Xingjian Du, Bilei Zhu, Zejun Ma, Ke Chen, Junbin Gao

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e. g., randomly adding or dropping edges and nodes).

Contrastive Learning Graph Classification +1

Wasserstein Adversarially Regularized Graph Autoencoder

1 code implementation • 9 Nov 2021 • Huidong Liang, Junbin Gao

This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric.

Clustering Link Prediction +1

How Neural Processes Improve Graph Link Prediction

1 code implementation • 30 Sep 2021 • Huidong Liang, Junbin Gao

Link prediction is a fundamental problem in graph data analysis.

Inductive Link Prediction Meta-Learning