activity
20172021
most citedUsing Graphlet Spectrograms for Temporal Pattern Analysis of Virus-Research Collaboration Networks

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DM2021

A systematic association of subgraph counts over a network

Dimitris Floros, Nikos Pitsianis, Xiaobai Sun

We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as…

cs.CY2021

Challenges in biomarker discovery and biorepository for Gulf-war-disease studies: a novel data platform solution

Dimitris Floros, Mulugu V. Brahmajothi, Alexandros-Stavros Iliopoulos +2

Aims: Our Gulf War Illness (GWI) study conducts combinatorial screening of many interactive neural and humoral biomarkers in order to establish predictive, diagnostic, and therapeu…

cs.SI20203 cited

Using Graphlet Spectrograms for Temporal Pattern Analysis of Virus-Research Collaboration Networks

Dimitris Floros, Tiancheng Liu, Nikos Pitsianis +1

We introduce a new method for temporal pattern analysis of scientific collaboration networks. We investigate in particular virus research activities through five epidemic or pandem…

cs.SI20201 cited

Fast Graphlet Transform of Sparse Graphs

Dimitris Floros, Nikos Pitsianis, Xiaobai Sun

We introduce the computational problem of graphlet transform of a sparse large graph. Graphlets are fundamental topology elements of all graphs/networks. They can be used as coding…

cs.LG2019

Spaceland Embedding of Sparse Stochastic Graphs

Nikos Pitsianis, Alexandros-Stavros Iliopoulos, Dimitris Floros +1

We introduce a nonlinear method for directly embedding large, sparse, stochastic graphs into low-dimensional spaces, without requiring vertex features to reside in, or be transform…

cs.LG2017

Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering

Nikos Pitsianis, Dimitris Floros, Alexandros-Stavros Iliopoulos +3

Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning alg…