arXiv subjects

Giulio Iannelli

Publications and source records attributed to Giulio Iannelli.

5 recordsLinked to original sources

arXiv2026

Chladni states in Ising Spin Lattices

Low-temperature spin dynamics can become trapped in long-lived patterns shaped by the geometry of the interaction network. Here we introduce Chladni states: spin configurations obtained by binarizing the eigenmodes of the interaction Laplacian. These graph-spectral patterns organize the metastable configurations reached by Ising systems under non-ergodic relaxation. The resulting Topological Mode Decomposition provides a compact way to monitor and reconstruct frozen spin configurations in ferromagnets, frustrated antiferromagnets, and spin glasses.

cond-mat.stat-mech↗

arXiv2026

Topological Symmetry Breaking in Antagonistic Dynamics

A dynamic concordia discors, a finely tuned equilibrium between opposing forces, is hypothesized to drive historical transformations. Similarly, a precise interplay of excitation and inhibition, often approximated by an 80:20 ratio, underlies the normal functionality of neural systems. In artificial neural networks, reinforcement learning enables the fine-tuning of internal signed connections, optimizing adaptive responses to complex stimuli and ensuring robust performance. Engineered systems with antagonistic interactions remain comparatively unexplored, particularly because their emergent phases are closely linked to frustration mechanisms in the underlying network. In this context, spin-glass theory has shown how apparently uncontrollable non-ergodic and chaotic behavior can arise from the complex interplay of competing interactions and frustration among units, leading to multiple metastable states that prevent the system from exploring all accessible configurations over time. Here, we show how topology constrains dynamics in systems with antagonistic interactions. We use the signed Laplacian operator to demonstrate how fundamental topological defects in lattices and networks percolate, shaping the geometrical structure and the complex energy landscape of the system. This reveals novel and highly robust multistable phases and establishes deep connections with spin glasses when thermal noise is considered, providing a natural topological and algebraic description of emergent multistability and non-ergodicity in frustrated systems.

cond-mat.dis-nn↗

arXiv2023

The effect of Collaborative-Filtering based Recommendation Algorithms on Opinion Polarization

A central role in shaping the experience of users online is played by recommendation algorithms. On the one hand they help retrieving content that best suits users taste, but on the other hand they may give rise to the so called "filter bubble" effect, favoring the rise of polarization. In the present paper we study how a user-user collaborative-filtering algorithm affects the behavior of a group of agents repeatedly exposed to it. By means of analytical and numerical techniques we show how the system stationary state depends on the strength of the similarity and popularity biases, quantifying respectively the weight given to the most similar users and to the best rated items. In particular, we derive a phase diagram of the model, where we observe three distinct phases: disorder, consensus and polarization. In the latter users spontaneously split into different groups, each focused on a single item. We identify, at the boundary between the disorder and polarization phases, a region where recommendations are nontrivially personalized without leading to filter bubbles. Finally, we show that our model can reproduce the behavior of users in the online music platform last.fm. This analysis paves the way to a systematic analysis of recommendation algorithms by means of statistical physics methods and opens to the possibility of devising less polarizing recommendation algorithms.

physics.soc-ph↗

arXiv2022

Effect of spatial correlations on Hopfield Neural Network and Dense Associative Memories

Hopfield model is one of the few neural networks for which analytical results can be obtained. However, most of them are derived under the assumption of random uncorrelated patterns, while in real life applications data to be stored show non-trivial correlations. In the present paper we study how the retrieval capability of the Hopfield network at null temperature is affected by spatial correlations in the data we feed to it. In particular, we use as patterns to be stored the configurations of a linear Ising model at inverse temperature $β$. Exploiting the signal to noise technique we obtain a phase diagram in the load of the Hopfield network and the Ising temperature where a fuzzy phase and a retrieval region can be observed. Remarkably, as the spatial correlation inside patterns is increased, the critical load of the Hopfield network diminishes, a result also confirmed by numerical simulations. The analysis is then generalized to Dense Associative Memories with arbitrary odd-body interactions, for which we obtain analogous results.

cond-mat.stat-mech↗

arXiv2022

Filter Bubble effect in the multistate voter model

Social media influence online activity by recommending to users content strongly correlated with what they have preferred in the past. In this way they constrain users within filter bubbles that strongly limit their exposure to new or alternative content. We investigate this type of dynamics by considering a multistate voter model where, with a given probability $λ$, a user interacts with a "personalized information" suggesting the opinion most frequently held in the past. By means of theoretical arguments and numerical simulations, we show the existence of a nontrivial transition between a region (for small $λ$) where consensus is reached and a region (above a threshold $λ_c$) where the system gets polarized and clusters of users with different opinions persist indefinitely. The threshold always vanishes for large system size $N$, showing that consensus becomes impossible for a large number of users. This finding opens new questions about the side effects of the widespread use of personalized recommendation algorithms.

physics.soc-ph↗