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…