Characterising Research Areas in the field of AI

“Characterising Research Areas in the field of AI” is a research paper submitted to the special track “Statistical Methods for Science Mapping” on “51st Scientific Meeting of the Italian Statistical Society”. Alessandra Belfiore1, Angelo Salatino2, Francesco Osborne2 1 Università della Campania Luigi Vanvitelli, Caserta (Italy) 2 Knowledge Media Institute, The Open University, Milton Keynes (UK)   Abstract … Read more

AUGUR: Forecasting the Emergence of New Research Topics

“AUGUR: Forecasting the Emergence of New Research Topics” is a paper submitted to the ACM/IEEE Joint Conference on Digital Libraries 2018, presented on June 5 2018, in Fort Worth, TX, USA   Angelo Salatino, Francesco Osborne and Enrico Motta   Abstract Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, … Read more

Export Graph in R via JSON

This post presents an easy solution for exporting and importing a graph object of igraph library.
In its previous versions, the library used to have the save and load functions in which you could respectively export and import the graph object [1]. Although they seem to not be in the library anymore, the documentation states:

“Attribute values can be set to any R object, but note that storing the graph in some file formats might result the loss of complex attribute values. All attribute values are preserved if you use save and load to store/retrieve your graphs.

The library also proposes write_graph and read_graph, that rely on the GraphML format, for exporting and importing back graph objects.

However, here I propose my little solution with almost zero options. It saves the graph and allows to re-load it again (in another session as well) simply saving all the fields and values in a JSON file.

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Clique Percolation Method in R: a fast implementation

Clique Percolation Method (CPM) is an algorithm for finding overlapping communities within networks, introduced by Palla et al. (2005, see references). This implementation in R, firstly detects communities of size k, then creates a clique graph. Each community will be represented by each connected component in the clique graph.

Algorithm

The algorithm performs the following steps:

1- first find all cliques of size k in the graph
2- then create graph where nodes are cliques of size k
3- add edges if two nodes (cliques) share k-1 common nodes
4- each connected component is a community

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