Publications (10)
Summarizing Labeled Multi-Graphs
Dimitris Berberidis, Pierre J. Liang, Leman Akoglu
Real-world graphs can be difficult to interpret and visualize beyond a certain size. To address this issue, graph summarization aims to simplify and shrink a graph, while maintaini…
Online Censoring for Large-Scale Regressions with Application to Streaming Big Data
Dimitris Berberidis, Vassilis Kekatos, Georgios B. Giannakis
Linear regression is arguably the most prominent among statistical inference methods, popular both for its simplicity as well as its broad applicability. On par with data-intensive…
Adaptive Bayesian Radio Tomography
Donghoon Lee, Dimitris Berberidis, Georgios B. Giannakis
Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collecte…
GraphSAC: Detecting anomalies in large-scale graphs
Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis
A graph-based sampling and consensus (GraphSAC) approach is introduced to effectively detect anomalous nodes in large-scale graphs. Existing approaches rely on connectivity and att…
Data-adaptive Active Sampling for Efficient Graph-Cognizant Classification
Dimitris Berberidis, Georgios B. Giannakis
The present work deals with active sampling of graph nodes representing training data for binary classification. The graph may be given or constructed using similarity measures amo…
Decentralized RLS with Data-Adaptive Censoring for Regressions over Large-Scale Networks
Zifeng Wang, Zheng Yu, Qing Ling +2
The deluge of networked data motivates the development of algorithms for computation- and communication-efficient information processing. In this context, three data-adaptive censo…