4 citations · 7 across the 6 of their papers we have counts for
6 papers
Exploring the structure and function of temporal networks with dynamic graphlets
Yuriy Hulovatyy, Huili Chen, Tijana Milenkovic
With the growing amount of available temporal real-world network data, an important question is how to efficiently study these data. One can simply model a temporal network as eith…
GREAT: GRaphlet Edge-based network AlignmenT
Joseph Crawford, Tijana Milenković
Network alignment aims to find regions of topological or functional similarities between networks. In computational biology, it can be used to transfer biological knowledge from a…
Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE
Yihan Sun, Joseph Crawford, Jie Tang +1
Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to comput…
Fair Evaluation of Global Network Aligners
Joseph Crawford, Yihan Sun, Tijana Milenković
Biological network alignment identifies topologically and functionally conserved regions between networks of different species. It encompasses two algorithmic steps: node cost func…
Identifying edge clusters in networks via edge graphlet degree vectors (edge-GDVs) and edge-GDV-similarities
Ryan W. Solava, Ryan P. Michaels, Tijana Milenkovic
Inference of new biological knowledge, e.g., prediction of protein function, from protein-protein interaction (PPI) networks has received attention in the post-genomic era. A popul…
Topological network alignment uncovers biological function and phylogeny
Oleksii Kuchaiev, Tijana Milenkovic, Vesna Memisevic +2
Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely ha…