TY - GEN
T1 - Spectral Evolution of Twitter Mention Networks
AU - Romero, Miguel
AU - Rocha, Camilo
AU - Finke, Jorge
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.
AB - This papers applies the spectral evolution model presented in [5] to networks of mentions between Twitter users who identified messages with the most popular political hashtags in Colombia (during the period which concludes the disarmament of the Revolutionary Armed Forces of Colombia). The model characterizes the dynamics of each mention network (i.e., how new edges are established) in terms of the eigen decomposition of its adjacency matrix. It assumes that as new edges are established the eigenvalues change, while the eigenvectors remain constant. The goal of our work is to evaluate various link prediction methods that underlie the spectral evolution model. In particular, we consider prediction methods based on graph kernels and a learning algorithm that tries to estimate the trajectories of the spectrum. Our results show that the learning algorithm tends to outperform the kernel methods at predicting the formation of new edges.
KW - Eigen decomposition
KW - Graph kernels
KW - Spectral evolution model
KW - Twitter mention networks
UR - http://www.scopus.com/inward/record.url?scp=85076690430&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36687-2_44
DO - 10.1007/978-3-030-36687-2_44
M3 - Conference contribution
AN - SCOPUS:85076690430
SN - 9783030366865
T3 - Studies in Computational Intelligence
SP - 532
EP - 542
BT - Complex Networks and Their Applications VIII - Volume 1 Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019
A2 - Cherifi, Hocine
A2 - Gaito, Sabrina
A2 - Mendes, José Fernendo
A2 - Moro, Esteban
A2 - Rocha, Luis Mateus
PB - Springer
T2 - 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
Y2 - 10 December 2019 through 12 December 2019
ER -