20 citations · 57 across the 7 of their papers we have counts for
7 papers
Modeling multi-scale data via a network of networks
Shawn Gu, Meng Jiang, Pietro Hiram Guzzi +1
Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher…
Analysis of Moral Judgement on Reddit
Nicholas Botzer, Shawn Gu, Tim Weninger
Moral outrage has become synonymous with social media in recent years. However, the preponderance of academic analysis on social media websites has focused on hate speech and misin…
Data-driven biological network alignment that uses topological, sequence, and functional information
Shawn Gu, Tijana Milenkovic
Many proteins remain functionally unannotated. Sequence alignment (SA) uncovers missing annotations by transferring functional knowledge between species' sequence-conserved regions…
Data-driven network alignment
Shawn Gu, Tijana Milenkovic
Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional kn…
Graphlets versus node2vec and struc2vec in the task of network alignment
Shawn Gu, Tijana Milenkovic
Network embedding aims to represent each node in a network as a low-dimensional feature vector that summarizes the given node's (extended) network neighborhood. The nodes' feature…
Pairwise versus multiple network alignment
Vipin Vijayan, Shawn Gu, Eric Krebs +2
Biological network alignment (NA) aims to identify similar regions between molecular networks of different species. NA can be local or global. Just as the recent trend in the NA fi…
From homogeneous to heterogeneous network alignment via colored graphlets
Shawn Gu, John Johnson, Fazle E. Faisal +1
Network alignment (NA) compares networks with the goal of finding a node mapping that uncovers highly similar (conserved) network regions. Existing NA methods are homogeneous, i.e.…