Search arXiv⌕ Search

arXiv · 2412.20904

Structural Changes and Percolation Transition in Networks after Aging Processes

Abstract

In social networking services, users constantly change, and the network structure changes simultaneously. As the network structure changes, so does the word-of-mouth within it. To study how information transfer on the network changes with the aging of the network, we investigated the relation of the structure and the percolation in the aged networks. We first prepared the Bianconi-Barabási model as the initial network and observed the time evolution of its properties by repeatedly deleting and adding nodes. We introduced two tunable parameters, the deleting parameter $α$ and the adding parameter $β$, and observed the aging behavior for the various parameter sets. We found that the network did not reach its steady state depending on the parameters. We also found that when the network is stable, there is a parameter region where the degree distribution changes from power-law to exponential decay. We performed the percolation analyses and found that the behavior of the percolation probability changes simultaneously with changes in the network's structure. This study is expected to help control network structure in steady-state aged networks.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ryuho Sekikawa, Hiroshi Watanabe. 2025-02-10. Structural Changes and Percolation Transition in Networks after Aging Processes. https://doi.org/10.7566/jpsj.94.044004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Optimality as a Generative Principle for Network Structure

Most real-world networks have evolved or been engineered to optimize some function, yet a unified framework for studying optimal networks across domains is lacking. We introduce GradNet, an AI-enabled framework that treats network topology as a continuously differentiable object, enabling the design and study of networks that optimize structural and dynamical objectives amenable to automatic differentiation under realistic constraints. We derive general optimality conditions, including an equimarginal principle for linear budgets, that make optimized networks analytically tractable. Canonical network features emerge spontaneously from constrained optimization: maximizing Kuramoto synchronization under coupling budgets yields sparse, bipartite, frequency-disassortative networks; minimizing social tension in opinion dynamics reproduces the factional split in Zachary's karate club; and maximizing communication capacity in spatial quantum networks under distance-dependent costs recovers minimum spanning trees. GradNet thus serves both as a network design tool scalable beyond $10^5$ nodes and as a scientific probe of structure-function relationships.

physics.soc-ph↗

Community-Centers Identify Robust Biomarker in High-Dimensional, Low-Sample-Size Gene Expression Data

High-dimensional, low-sample-size bulk gene expression data poses a fundamental challenge in transcriptomics, which typically includes tens of thousands of genes but relatively few samples, leading to overfitting and unstable selection for key features as biomarkers. We propose a regression by community centers (RCC) framework tailored for such data. RCC converts gene expression data into a feature proximity network and leverages the phase transition of the giant connected component to identify a critical distance threshold that yields a sparse yet maximally informative network representation, indicated by a minimum normalized shortest compression length. Intuitively, at the criticality, the network balances fragmentation and over-connectivity, allowing meaningful gene-gene associations communities to emerge while filtering noises, such that community-centers capture the most informative and non-redundant signals. These representative genes are then fed to a simple ordinary least squares (OLS) model for downstream predictions. Across five bulk gene expression datasets, RCC displays exceptional prediction performance, clearly outperforming benchmark methods, remains robust to missing data and noises, and does not rely on external biological knowledge. These results suggest that network-based representations provide an effective and general framework for high-dimensional, low-sample-size prediction tasks beyond transcriptomics and illustrate how network-science ideas can support robust learning in data-scarce regimes.

physics.soc-ph↗

The efficacy-competitiveness frontier in the Swiss system

An ideal tournament is both efficacious and attractive: it selects the best player as the winner, but only at the end of the tournament in order to maintain suspense as long as possible. We identify and explore the trade-off between these criteria in Swiss-system chess team tournaments via simulations. Organising more rounds is shown to improve efficacy but increase the probability of a stakeless last round. The 2008 reform in the Chess Olympiad ranking is found to be a questionable move, as the previous rule based on board points determines the efficacy-competitiveness frontier, regardless of whether two or three points are awarded for winning a match. Combining board points and match points is better than using match points only. Our results can help optimise the design of the Swiss system, which, besides chess, is also widely used in e-sports.

physics.soc-ph↗