Complex Network Analysis Techniques

Detection of spreader nodes and ranking of interacting edges in Human-SARS-CoV protein interaction network

The paper identifies spreader nodes and ranks interacting edges in the SARS-CoV-human protein interaction network using a new spreadability index and validates it with the SIS model.

Spreading predictability in complex networks

This paper presents a probability-based model to predict infection nodes from a snapshot in spreading dynamics, showing precise prediction on synthetic and real networks.

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.

Constructing Graphs from Genetic Encodings

The paper introduces a framework for generating network topologies based on genetic-inspired compatibility rules, demonstrating its ability to produce structured graphs and a Random Genetic family with distinct properties.

Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

This paper introduces a method to classify neurons based on their connectivity using a stochastic block model and spectral embedding, achieving accurate clustering even with noise and edge swaps.

hcga: Highly Comparative Graph Analysis for network phenotyping

The paper introduces HCGA, a framework for analyzing graph data by computing thousands of graph features and using statistical learning tools to improve classification and discovery.

Scarcity of scale-free topology is universal across biochemical networks

The paper analyzes biochemical networks from individual and ecosystem-level data to show they are not scale-free, with no sharp transition in network structure across levels.

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.

Link Prediction through Deep Generative Model

This paper introduces a deep generative model for link prediction that works across different network types and domains, showing superior performance compared to existing methods.

IHS: an integrative method for the identification of network hubs

The paper introduces IHS, a novel algorithm that integrates network centrality measures to identify hub nodes, outperforming existing methods in validated biological networks.

functionInk: An efficient method to detect functional groups in multidimensional networks reveals the hidden structure of ecological communities

The paper introduces functionInk, a method to detect functional groups in multidimensional networks, revealing ecological community structures by identifying modules and guilds.

Metrics for Graph Comparison: A Practitioner’s Guide

This paper compares graph distance measures and introduces a Python library to evaluate their effectiveness in analyzing graph structures across different scales.

Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis

The paper analyzes interactions between pharmaceutical companies, countries, cancers, and rare diseases using Wikipedia network data and the REGOMAX algorithm.

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.

Wikipedia network analysis of cancer interactions and world influence

The paper uses Wikipedia's network to analyze cancer-drug-country interactions, finding strong agreement between drug-cancer links and medical prescriptions.

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.

Organization in complex brain networks: energy distributions and phase shift

The paper analyzes brain network energy distribution using a Hamiltonian function and identifies phase transitions linked to brain states through a resolution parameter.

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.

CINNA: An R/CRAN package to decipher Central Informa-tive Nodes in Network Analysis

CINNA compares centrality measures to identify the most informative nodes in networks using dimensionality reduction.

Probing the network structure of health deficits in human aging

The paper models human aging as a stochastic network of health attributes and shows that network structure influences the recovery of observational health data.

Prioritizing network communities

This paper introduces a method to prioritize network communities for experimental validation, showing significant improvement in prioritization efficiency.

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.

Efficient Detection of Communities in Biological Bipartite Networks

The paper introduces an efficient algorithm for community detection in biological bipartite networks, showing improved speed and comparable quality to existing methods.

On The Structure Of Cortical Micro-Circuits Inferred From Small Sample Sizes

The paper shows that small sample sizes make it hard to distinguish different network topologies in cortical circuits and introduces a new statistic to overcome this limitation.

Breakdown Of Modularity In Complex Networks

This paper introduces a model to study modularity breakdown in networks and finds that modularity breakdown is an adaptive feature under functional and cost constraints.