Complex Network Analysis Techniques

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.

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.

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.

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.

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.

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.

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.

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.

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.

Circuit motifs and graph properties of connectome development in C. elegans

The paper analyzes C. elegans connectomes across developmental stages using graph and motif analysis to identify evolving network properties and stable patterns.

Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

The paper introduces a hypergraph-based method to detect communities in cell-to-cell communication networks using ligand-receptor interactions and validates it on synthetic and real datasets.

Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification

The paper introduces a link prediction model using ERGM with outcome misclassification to handle incomplete network data, showing improved performance on social and brain networks.

Reducing module size bias of participation coefficient

The paper introduces a normalized participation coefficient to reduce module size bias in network analysis, demonstrating improved identification of high-participation nodes in various networks.

Adaptive rewiring based on diffusion balances stability and plasticity in weighted networks while evolving ‘brain-like’ structure

The paper models brain-like network evolution through adaptive rewiring based on diffusion, showing how weighted networks can balance stability and flexibility by adjusting diffusion rates.

Eigenvector-based community detection for identifying information hubs in neuronal networks

The paper uses eigenvector-based community detection to identify information hubs in neuronal networks of C. elegans, macaques, and humans, revealing previously unrecognized structural insights.

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

The paper introduces a network automaton rule, CH2-L3, to predict complex network structures across different domains like protein interactomes and food webs by maximizing internal links in local communities.