10.1k citations · 17.4k across the 69 of their papers we have counts for
9 papers · 1 filter
Towards Data-centric Graph Machine Learning: Review and Outlook
Xin Zheng, Yixin Liu, Zhifeng Bao +4
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent yea…
Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification
Xiao Shen, Mengqiu Shao, Shirui Pan +2
Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from di…
ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Linhao Luo, Jiaxin Ju, Bo Xiong +3
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graph…
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learnin…
Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Bo Xiong, Mojtaba Nayyer, Shirui Pan +1
Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a triple. A significant amount of important kno…
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang +8
Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on l…
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain
Fatemeh Shiri, Teresa Wang, Shirui Pan +5
International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely relate…
Geometric Relational Embeddings: A Survey
Bo Xiong, Mojtaba Nayyeri, Ming Jin +4
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for str…
Neighbor Contrastive Learning on Learnable Graph Augmentation
Xiao Shen, Dewang Sun, Shirui Pan +2
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt h…