Network Sparsification

Network sparsification with a variety of novel and known network sparsification techniques. All network sparsification techniques reduce the number of edges, not the number of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. This package is based on the work of Spielman, D., Srivastava, N. (2009). Koutis I., Levin, A., Peng, R. (2013). Toivonen, H., Mahler, S., Zhou, F. (2010). Foti, N., Hughes, J., Rockmore, D. (2011).

Integration Network

It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" .

Clustering for networks

Facilitates network clustering and evaluation of cluster configurations.

Network Explorer

Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface; 2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.

ROBustness in Network

Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) < https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.

Geographic Networks

Provides classes and methods for handling networks or graphs whose nodes are geographical (i.e. locations in the globe). The functionality includes the creation of objects of class geonetwork as a graph with node coordinates, the computation of network measures, the support of spatial operations (projection to different Coordinate Reference Systems, handling of bounding boxes, etc.) and the plotting of the geonetwork object combined with supplementary cartography for spatial representation.

Statistical Network Analysis of Animal Social Networks

Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.

Statistical Network Models for Dynamic Network Data

Tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) , Stadtfeld, Hollway, and Block (2017b) , Stadtfeld and Block (2017) , Hoffman et al. (2020) .

Parallel Mutual Information Estimation for Gene Network Reconstruction

Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 ).

Software Tools to Quantify Structural Importance of Nodes in a Network

Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of constraint and effective network size by Burt (2000) ; algorithm to identify key players by Borgatti (2006) ; and the bridging algorithm by Valente and Fujimoto (2010) . On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured.