arXiv subjects

Tania Ghosh

Publications and source records attributed to Tania Ghosh.

3 recordsLinked to original sources

arXiv2026

Hierarchical Community Detection in Bipartite Networks

Many bipartite networks exhibit hierarchical community structure, but existing community detection methods are not well-suited for detecting hierarchy. They also do not effectively handle weighted bipartite networks. In this work, we introduce a novel modularity-based objective function, called the generalized bipartite modularity density, Qbg, specifically designed for hierarchical community detection in bipartite systems. The framework incorporates a tunable resolution parameter that enables systematic exploration of community structure across multiple scales. It leverages resolution-limit behavior in bipartite networks as a tool to uncover hierarchical organization without projecting the network or altering its intrinsic bipartite topology. We evaluate the method using a hierarchical synthetic bipartite benchmark and apply it to two empirical networks. In all cases, Qbg recovers established mesoscale structure while revealing additional hierarchical and fine-scale organization beyond that detected by conventional bipartite approaches. These results establish Qbg as a flexible, interpretable, and resolution-aware framework for hierarchical community detection in bipartite networks.

cs.SI↗

arXiv2025

Experimental verification of Generalised Synchronization

Generalized synchronization (GS) describes a state in which two coupled dynamical systems exhibit a functional relationship between their variables. GS can be achieved by appropriately designing the coupling to constrain the dynamics onto an invariant submanifold, a concept well established in theory. However, experimental validation remains crucial. In this work, we experimentally demonstrate GS using coupled chaotic Lorenz oscillators, implementing unidirectional coupling where the state variables of the slave oscillator are scaled according to a predefined relationship with those of the master oscillator.

nlin.CD↗

arXiv2024

Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL)

Arguably, the most fundamental problem in Network Science is finding structure within a complex network. One approach is to partition the nodes into communities that are more densely connected than one expects in a random network. "The" community structure then corresponds to the partition that maximizes Modularity, an objective function that quantifies this idea. Finding the maximizing partition, however, is a computationally difficult, NP-Complete problem. We explore using a recently introduced machine-learning algorithmic scheme to find the structure of benchmark networks. The scheme, known as RenEEL, creates an ensemble of $K$ partitions and updates the ensemble by replacing its worst member with the best of $L$ partitions found by analyzing a simplified network. The updating continues until consensus is achieved within the ensemble. We perform an empirical study of three real-world networks to explore how the Modularity of the consensus partition depends on the values of $K$ and $L$ and relate the results to the extreme value statistics of record-breaking. We find that increasing $K$ is generally more effective than increasing $L$ for finding the best partition.

physics.comp-ph↗