We address these shortcomings by exploring motif-based weighted spectral clustering methods. 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 ...
We address these shortcomings by exploring motif-based weighted spectral clustering methods. 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 ...
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...
capturehigher-orderinteractionsbetweennodeswork,weproposemoti-basedgraphattentionmodel,calledMotiConvolutionalNetworks,whichgeneralizespastapproachesusingweightedmulti-hopmotiadjacencymatricescapturehigher-orderneighborhoods.Anovelattentionmechanismisusedalloweachindividualnodemostrelevantneighborhoodapplyitsflter.Weevaluateour...
where\({k}_{j}^{{\rm{in}}}={\sum }_{i}{a}_{ij}\)is the number of prey of speciesj(also known as thein-degree) andaijare entries of the adjacency matrixAof the food web. Here the convention is that the directed trophic links point from preyito predatorj. ...
[41,42] with protein-DNA interactions from MotifMap can yield a more comprehensive view of molecular mechanisms and networks. Integration of PPI data can also facilitate the identification of transcriptional complexes. For example, evidence for a complex based on adjacency of binding sites for two ...
W is an adjacency matrix where W_ij=1 if there is a connection from node j onto node i and is zero otherwise. The parameter p is the probability of a single edge, i.e., p = P(W_ij = 1) for any nodes i != j. The generation algorithm requires that 0 < p <= 0.5. The ...
Subsequently, we design a simplified attention mechanism, allowing embeddings carrying motif high-order features to guide the representation of embeddings based on adjacency features. We then employ a feed-forward neural network to optimize node embeddings. Specifically, this framework addresses the issue...
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...
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. Branches The main branch contains stable ver...