Complex Network Analysis Techniques

Fragmented social networks promote complex behavioural contagions over infectious disease spread

The paper models how fragmented social networks affect the spread of social behaviors versus infectious diseases, finding that complex contagions are less hindered by subgroup structure than simple contagions.

Tree-to-cycle transition scale characterizes trade-off between dissipation and construction cost in adaptive transport networks

The paper introduces a metric to quantify the trade-off between dissipation and construction cost in adaptive transport networks using a single value derived from network architecture.

The Friendship Paradox across animal social systems is governed by network structure and biological features

The paper analyzes relationship disparity across 391 animal social networks to show how network structure and biological attributes influence the Friendship Paradox.

sabinaHSBM: An R package for link prediction network reconstruction using Hierarchical Stochastic Block Models

sabinaHSBM reconstructs ecological networks from incomplete data using hierarchical stochastic block models, identifying undocumented links and improving network accuracy.

Developmental Continuity of Brain Network Core Organization in C. elegans

The paper studies the developmental changes in the brain connectome of C. elegans, revealing conserved and dynamic components in its core organization and club structure.

Evolving Social Context Regulates Contagion Spread in Multiplex Networks

This paper develops a multiplex-network framework that couples contagion dynamics with evolving social context, showing how social responses can reshape spreading dynamics and generate resilience.

Clustering Compresses Attractors in Watts-Strogatz Threshold Boolean Networks

The paper investigates whether higher clustering in Watts-Strogatz networks reduces attractor periods, finding a robust 13-15% decrease per 0.10 increase in clustering coefficient.

Signed motif analysis of the Caenorhabditis elegans neuronal network reveals positive feedforward and negative feedback loops

The paper analyzes the C. elegans neuronal network using signed motif analysis to identify overrepresented positive feedforward and negative feedback loops.

Weight distributions in the fruit-fly and the mouse connectomes

The paper analyzes synaptic weight distributions in the mouse visual cortex and fruit-fly optic lobe, finding power-law behavior globally and heavy-tailed local node strengths, with implications for learning mechanisms.

Structural selection is revealed only at meso-structural scales

The paper shows structural selection is detectable only at meso-structural scales in ecological networks, revealing evolutionary gradients lost in aggregated analyses.

Controlling Reciprocity in Binary and Weighted Networks: A Novel Density-Conserving Approach

This paper introduces Network Reciprocity Control algorithms to adjust asymmetry and reciprocity in networks while preserving density and weight, demonstrating their impact on network properties and scalability.

The Dominance of Geometric Graph Models in Animal Social Networks

This study finds that geometric graphs are the most frequently predicted model for animal social networks across species and data collection methods.

An entropic measure of diverse specialisation highlights multifunctional neurons in annotated connectomes

The paper introduces an entropic measure to identify multifunctional neurons by combining topological analysis with node metadata in connectomes of C. elegans and Drosophila.

Benchmarking overlapping community detection methods for applications in human connectomics

The paper benchmarks overlapping community detection methods on human connectomes, finding OSLOM best identifies overlapping structures and reveals hierarchical cortical organization.

Extensive Benchmarking of Community Detection Algorithms

This paper benchmarks community detection algorithms on artificial and real-world networks, finding performance declines with lower modularity and trade-offs between accuracy and computational time.

Physical Network Constraints Define the Lognormal Architecture of the Brain's Connectome

The paper analyzes eight connectomes and finds their degree distributions follow lognormal patterns, attributing this to a stochastic multiplicative process governing neuron size.

Accounting for Edge Uncertainty in Stochastic Actor-Oriented Models for Dynamic Network Analysis

The paper extends Stochastic Actor-Oriented Models to account for edge uncertainty in dynamic networks, improving estimation accuracy with simulated and real brain network data.

Functional Control of Network Dynamical Systems: An Information Theoretic Approach

The paper develops a framework for designing perturbation signals to achieve desired information routing patterns in complex networks, applying it to brain stimulation and gene regulatory networks.

A simulated annealing algorithm for randomizing weighted networks

This paper introduces a simulated annealing algorithm for randomizing weighted networks while preserving strength sequences, outperforming existing methods and showing broad applicability in neuroscience and beyond.

Reduced spread of nodes in spatial network models improves topology associated with increased computational capabilities

This study uses spatial network models to show that wiring cost and spatial clustering influence network topology, with long-range connections improving global communication and resilience to pruning.

Measuring the functional complexity of nanoscale connectomes: polarity matters

The paper develops a statistic called XORness to measure functional complexity in nanoscale connectomes and shows it is high in both real and generated connectomes with similar network statistics.

Heavy-tailed neuronal connectivity arises from Hebbian self-organization

The paper shows that heavy-tailed neuronal connectivity and clustering emerge from a minimal model of Hebbian self-organization, confirmed in animal connectomes.

The local topology of dynamical network models for biology

This paper analyzes the topology of dynamic biological network models and finds they cluster into two classes, one resembling sensory transmission networks and the other resembling word-adjacency networks.

Data-driven motif discovery in biological neural networks

This paper introduces a motif discovery method for biological networks that identifies statistically significant subgraphs from single network examples, addressing challenges in computational cost and statistical significance testing.

Weighted transitivity scores that account for triadic edge similarity in undirected graphs

The paper introduces weighted transitivity scores for undirected graphs that consider triadic edge similarity, demonstrating their biological relevance in animal contact networks.

Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs

The paper identifies the metric backbone as a subgraph of social contact networks that preserves shortest paths and community structure, revealing its role in epidemic transmission and network robustness.

Structure and dynamics of human disease-complication network

The paper compiles and analyzes the Human Disease-Complication Network to uncover its structure, dynamics, and functional motifs.

From components to communities: bringing network science to clustering for genomic epidemiology

The paper introduces community detection methods to improve clustering of infectious disease sequences, demonstrating their application to HIV-1 data to better capture transmission dynamics.

A Weighted Network Analysis Framework for the Hourglass Effect - and its Application in the C. Elegans Connectome

The paper introduces a weighted network analysis framework to study the hourglass effect in the C. elegans connectome, revealing sex-specific differences in core neuron locations.

TSCDA: A novel greedy approach for community discovery in networks

The paper introduces a greedy algorithm for community detection in networks, showing it is competitive with and sometimes outperforms existing methods.