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20102023
most citedA Comprehensive Survey on Graph Anomaly Detection with Deep Learning

839 citations · 1k across the 18 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.AI2021

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

Denghui Zhang, Zixuan Yuan, Hao Liu +2

Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge gra…

cs.LG2021★ 1 cited

Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis

Zixuan Yuan, Yada Zhu, Wei Zhang +3

Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in…

cs.CR2021★ 1 cited

Cyberphysical Sequencing for Distributed Asset Management with Broad Traceability

Joshua Siegel, Gregory Falco

Cyber-Physical systems (CPS) have complex lifecycles involving multiple stakeholders, and the transparency of both hardware and software components' supply chain is opaque at best.…

cs.CV2021★ 1 cited

Information Theory-Guided Heuristic Progressive Multi-View Coding

Jiangmeng Li, Wenwen Qiang, Hang Gao +5

Multi-view representation learning captures comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning (CL) to learn rep…

cs.LG2021★ 839 cited

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Xiaoxiao Ma, Jia Wu, Shan Xue +5

Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasin…

cs.LG2021★ 89 cited

Heterogeneous Graph Representation Learning with Relation Awareness

Le Yu, Leilei Sun, Bowen Du +3

Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link predicti…