85 citations · 228 across the 10 of their papers we have counts for
10 papers
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…
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…
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs
Dimitris Berberidis, Georgios B. Giannakis
Node embedding is the task of extracting informative and descriptive features over the nodes of a graph. The importance of node embeddings for graph analytics, as well as learning…
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…
Adaptive Diffusions for Scalable Learning over Graphs
Dimitris Berberidis, Athanasios N. Nikolakopoulos, Georgios B. Giannakis
Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. T…
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…
Data Sketching for Large-Scale Kalman Filtering
Dimitris Berberidis, Georgios B. Giannakis
In an age of exponentially increasing data generation, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The pre…
Large-scale Kernel-based Feature Extraction via Budgeted Nonlinear Subspace Tracking
Fatemeh Sheikholeslami, Dimitris Berberidis, Georgios B. Giannakis
Kernel-based methods enjoy powerful generalization capabilities in handling a variety of learning tasks. When such methods are provided with sufficient training data, broadly-appli…
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…