16 citations · 17 across the 4 of their papers we have counts for
6 papers
Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification
Miguel Romero, Oscar Ramírez, Jorge Finke +1
Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.g., biological processes) of a specific organism. Despite recent advances, th…
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
Miguel Romero, Jorge Finke, Camilo Rocha
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction…
Characterizing the head of the degree distributions of growing networks
Jan Medina-López, Jorge Finke
The analysis in this paper helps to explain the formation of growing networks with degree distributions that follow extended exponential or power-law tails. We present a generic mo…
Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction
Miguel Romero, Jorge Finke, Camilo Rocha +1
The spectral evolution model aims to characterize the growth of large networks (i.e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the…
A Random Network Model for the Analysis of Blockchain Designs with Communication Delay
Carlos Pinzón, Camilo Rocha, Jorge Finke
This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. The model…
Estimating Formation Mechanisms and Degree Distributions in Mixed Attachment Networks
Jan Medina, Jorge Finke, Camilo Rocha
Our work introduces an approach for estimating the contribution of attachment mechanisms to the formation of growing networks. We present a generic model in which growth is driven…