Complex Systems-RCB

The preliminary work highlights the need for encouraging stronger collaborations among the researchers, not only from academia but from other sectors, to align with the targets outlined in the national science and technology agenda.

Using data from Scopus limited to researchers from 5 universities offering doctoral degrees in physics, we observe faster-than-linear trends in productivity from 1985-2024. The distribution of co-author and co-authorship counts follow robust power-laws over the last two decades.

We studied the co-authorship network of a pioneer community of Philippine physics researchers:

K.M.A. Aguana and R.C. Batac, Emergence of power-law statistics in the co-authorship networks of Philippine physics researchers, Scientometrics, doi.org/10.1007/s111... (2026).

Our results recover power-law distributions of neuronal avalanches, with critical scaling exponents close to 3/2. The resulting rewired network also exhibits the log-normal distributions of node degrees, and the power-law distributions of edge weights with exponents close to 3.

In our model, the sandpile model of self-organized criticality is imposed on hierarchical modular networks. Hebbian learning is imposed during avalanche events, as toppled sites are rewired to have stronger connections. Random prunings are also introduced during stasis times.

Empirical studies of brain networks, or connectomes, of various species reveal log-normal degree distributions and power-law edge weight distributions between functional elements. We attribute these statistical signatures to the brain criticality hypothesis.

We model brain network structures using sandpiles with learning on hierarchical modular networks.
M.T. Cirunay, R.C. Batac, and G. Ódor, Learning and criticality in a self-organizing model of connectome growth, Sci Rep 15, 31890 (2025). doi.org/10.1038/s415...

We also observe the hierarchical scaling of Philippine cities based on population, which is a consequence of the Zipfian statistics. On the other hand, Gibrat's law is not observed, as the growth rates of the cities are found to be not independent of their populations.

Census data from the last 20 years manifest power-law statistics for the top 30% of cities/municipalities that hold 70% of the population. Population distributions follow the Pareto law with exponents close to 2.5, and rank-size trends follow Zipf's law with exponents of 0.7.

Our new paper examines the power-law signatures in the statistics of populations of Philippine cities and municipalities:

D.M.T. Ordoñez and R.C. Batac, Testing the validity of Zipf and Pareto laws: A multi-method approach, Physica A, doi.org/10.1016/j.ph...