607 citations · 1.2k across the 34 of their papers we have counts for
7 papers · 1 filter
eTREE: Learning Tree-structured Embeddings
Faisal M. Almutairi, Yunlong Wang, Dong Wang +2
Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representati…
GAGE: Geometry Preserving Attributed Graph Embeddings
Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos
Node embedding is the task of extracting concise and informative representations of certain entities that are connected in a network. Various real-world networks include informatio…
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity
Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos
Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset o…
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos
Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predica…
PHASED: Phase-Aware Submodularity-Based Energy Disaggregation
Faisal M. Almutairi, Aritra Konar, Ahmed S. Zamzam +1
Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing ene…
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods
Aritra Konar, Nicholas D. Sidiropoulos
Mining dense subgraphs is an important primitive across a spectrum of graph-mining tasks. In this work, we formally establish that two recurring characteristics of real-world graph…