arXiv subjects

Meritxell Vila-Miñana

Publications and source records attributed to Meritxell Vila-Miñana.

2 recordsLinked to original sources

arXiv2026

Efficient generation of networks with minimal average shortest-path distance

Designing networks that minimize distances and satisfy structural constraints is a fundamental task across transportation, communication, and biological systems. Here, we consider the problem of finding, for a given degree sequence, the network structure displaying the smallest possible average shortest-path length. While exact solutions are available in linear time for trees, such an optimization problem becomes computationally infeasible as soon as loops are allowed in the networks. We propose a fast algorithm to construct approximate solutions to such a degree-constrained distance-minimization problem. Accordingly, edges are first created between high-degree nodes; then, additional connections are placed following the rules of the standard configuration model. In spite of its simplicity, the algorithm displays outstanding performance as demonstrated in our systematic experiments on both synthetic and real degree sequences. Our method is particularly effective on synthetic degree sequences displaying medium levels of heterogeneity. When applied to degree sequences of real networks, the proposed algorithm is able to reduce the all-pair shortest path of real structures by 20%, on average. We perform a validation on small-sized networks, where we compare the shortest-path distance of the networks generated with our algorithm against those obtained via simulated annealing optimization. Although simulated annealing yields slightly better structures, our proposed algorithm provides nearly identical solutions at a substantially lower computational cost, making it a solid method in applications concerning large-scale systems.

physics.soc-ph↗

arXiv2026

Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction

Determining whether two graphs are isomorphic is a fundamental problem with practical applications in areas such as molecular chemistry or social network analysis, yet it remains a challenging task, with exact solutions often being computationally expensive. We address this task using persistent homology built on motif-based filtrations of graphs, a method from topological data analysis that summarizes the shape of data by tracking the persistence of structural features along filtrations. Specifically, we use edge-weighting schemes based on the densities of triangles, chordless squares, and chordless pentagons, which have been shown to be effective for detecting network dimensionality. Our cycle-density filtrations distinguish non-isomorphic graphs perfectly or nearly perfectly across four demanding graph families, many of which exhibit symmetries. We outperform curvature-based, degree-based, and Vietoris--Rips filtrations, and match or exceed the accuracy of egonet-distance methods while incurring a lower computational cost. The expressive power of our filtrations goes beyond isomorphism testing: because they capture rich structural information from graphs, they consistently achieve top performance on property prediction tasks using real-world data, and exhibit high sensitivity to edge rewiring and removal. Together, these findings establish cycle-density filtrations as an effective and computationally tractable framework for graph comparison and characterization, bridging topological data analysis and network science.

math.AT↗