We present new and computationally useful matrix formulae for motif adjacency matrices on weighted networks, which can be used to construct efficient algorithms for any anchored or non-anchored motif on three nodes. In a very sparse regime, our proposed method can handle graphs with five million ...
We present new and computationally useful matrix formulae for motif adjacency matrices on weighted networks, which can be used to construct efficient algorithms for any anchored or non-anchored motif on three nodes. In a very sparse regime, our proposed method can handle graphs with five million ...
Building motif adjacency matrices Sampling random weighted directed networks Spectral embedding with motif adjacency matrices Motif-based spectral clustering The methods are all designed to run quickly on large sparse networks, and are easy to install and use. ...
capturehigher-orderinteractionsbetweennodeswork,weproposemoti-basedgraphattentionmodel,calledMotiConvolutionalNetworks,whichgeneralizespastapproachesusingweightedmulti-hopmotiadjacencymatricescapturehigher-orderneighborhoods.Anovelattentionmechanismisusedalloweachindividualnodemostrelevantneighborhoodapplyitsflter.Weevaluateour...
Specifically, to capture temporal information and higher-order features, we develop a method extracting temporal motif instances from temporal networks, and design an algorithm to compute the weighted motif adjacency matrix and the diagonal motif out-degree matrix, then define a motif transition matrix...
Spectral embedding with motif adjacency matrices Motif-based spectral clustering The methods are all designed to run quickly on large sparse networks, and are easy to install and use. Branches The main branch contains stable versions. The develop branch may be unstable, and is for development purpo...