GraphContrast IoT for self supervised graph based anomaly detection in multivariate industrial IoT sensor networks

Authors

Institutions

Publication Details

Journal
Discover Computing
Published
2026-11-09
DOI
https://doi.org/10.1007/s10791-026-10794-7
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

GraphContrast IoT for self supervised graph based anomaly detection in multivariate industrial IoT sensor networks

M. V. Vijaya Saradhi, Ch. Bhavani, Katta Nagaraju, N. Murali Krishna et al.
Discover Computing
Anomaly Detection Techniques and Applications
article

GraphContrast IoT for self supervised graph based anomaly detection in multivariate industrial IoT sensor networks

M. V. Vijaya Saradhi, Ch. Bhavani, Katta Nagaraju, N. Murali Krishna, Kiran Kumar Reddy Penubaka, Sandhya Rani Sarlana
article en

Abstract

No abstract available for this paper.
Discover ComputingVol. 29(1)
Aditya University (IN)
Anomaly Detection Techniques and Applications
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
GraphContrast IoT for self supervised graph based anomaly detection in multivariate industrial IoT sensor networks — M. V. Vijaya Saradhi, Ch. Bhavani, et al. · Discover Computing (2026) | TGRS Research Map | TGRS