Výsledky vyhledávání - "Lee Meng-Chieh"

Report

TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection

Autor: Deforce, Boje, Lee, Meng-Chieh, Baesens, Bart, Asensio, Estefanía Serral, Yoo, Jaemin, Akoglu, Leman

Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types

Externí odkaz: http://arxiv.org/abs/2404.02865

Zobrazit plný text záznamu

Report

NetInfoF Framework: Measuring and Exploiting Network Usable Information

Autor: Lee, Meng-Chieh, Yu, Haiyang, Zhang, Jian, Ioannidis, Vassilis N., Song, Xiang, Adeshina, Soji, Zheng, Da, Faloutsos, Christos

Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well? More specifically, do the graph structure and the node features carry enough usable information f

Externí odkaz: http://arxiv.org/abs/2402.07999

Zobrazit plný text záznamu

Report

Descriptive Kernel Convolution Network with Improved Random Walk Kernel

Autor: Lee, Meng-Chieh, Zhao, Lingxiao, Akoglu, Leman

Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite of Kernel Convolution Networks (KCNs) successfully revitalized graph ke

Externí odkaz: http://arxiv.org/abs/2402.06087

Zobrazit plný text záznamu

Report

NetEffect: Discovery and Exploitation of Generalized Network Effects

Autor: Lee, Meng-Chieh, Shekhar, Shubhranshu, Yoo, Jaemin, Faloutsos, Christos

Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance

Externí odkaz: http://arxiv.org/abs/2301.00270

Zobrazit plný text záznamu

Report

Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining

Autor: Yoo, Jaemin, Lee, Meng-Chieh, Shekhar, Shubhranshu, Faloutsos, Christos

How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limit

Externí odkaz: http://arxiv.org/abs/2210.04081

Zobrazit plný text záznamu

Report

gen2Out: Detecting and Ranking Generalized Anomalies

Autor: Lee, Meng-Chieh, Shekhar, Shubhranshu, Faloutsos, Christos, Hutson, T. Noah, Iasemidis, Leon

In a cloud of m-dimensional data points, how would we spot, as well as rank, both single-point- as well as group- anomalies? We are the first to generalize anomaly detection in two dimensions: The first dimension is that we handle both point-anomalie

Externí odkaz: http://arxiv.org/abs/2109.02704

Zobrazit plný text záznamu

Report

AutoAudit: Mining Accounting and Time-Evolving Graphs

Autor: Lee, Meng-Chieh, Zhao, Yue, Wang, Aluna, Liang, Pierre Jinghong, Akoglu, Leman, Tseng, Vincent S., Faloutsos, Christos

How can we spot money laundering in large-scale graph-like accounting datasets? How to identify the most suspicious period in a time-evolving accounting graph? What kind of accounts and events should practitioners prioritize under time constraints? T

Externí odkaz: http://arxiv.org/abs/2011.00447

Zobrazit plný text záznamu