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Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship ...

A bipartite graph contains inter-set edges between two disjoint vertex sets, and is widely used to model real-world data, such as user-item purchase records, author-article publications, and biological interactions between drugs and proteins. k-Bipartite ...

Radio frequency identification technology has been rapidly embraced throughout different wireless communication domains as one of the primary identification solutions for the internet of things. Most present RFID authentication techniques are susceptible ...

Given a graph \mathcalG , the spanning centrality (SC) of an edge e measures the importance of e for \mathcalG to be connected. In practice, SC has seen extensive applications in computational biology, electrical networks, and combinatorial ...

Hypergraphs are an omnipresent data structure used to represent high-order interactions among entities. Given a hypergraph H wherein nodes are associated with attributes, attributed hypergraph clustering (AHC) aims to partition the nodes in H into k ...

We identify and correct excess dispersion in the leading eigenvector of a sample covariance matrix when the number of variables vastly exceeds the number of observations. Our correction is data-driven, and it materially diminishes the substantial impact of ...

K-Nearest Neighbours (KNN) is one of the fundamental classification methods in machine learning. The performance of KNN method is restricted by the number of neighbours k. It is obvious that the outliers appear when dealing with small data samples. In ...

Let $P : \Omega \subset {\mathbb C} \rightarrow {\mathbb C}^{n\times n}$ be given by $P(\lambda) :=\sum^m_{j=0}A_j\phi_j(\lambda),$ where $ \phi_j : \Omega \rightarrow {\mathbb C}$ for $j=0, 1, \ldots, m$ are suitable functions. We present an ...

Recently, large amount data streams are increasing. It is difficult to continuously store data and perform the principal component analysis in periodical offline (batch) mode. To solve this problem, there is a need to reflect the concept drift through ...

We present a general framework for the sensitivity and backward perturbation analysis of linear as well as nonlinear multiparameter eigenvalue problems (MEPs). For a general norm on the space of MEPs, we present a comprehensive analysis of the sensitivity ...

Eigenvector-based centrality measures are among the most popular centrality measures in network science. The underlying idea is intuitive and the mathematical description is extremely simple in the framework of standard, mono-layer networks. Moreover, ...

We introduce generalized Fiedler (GF) pencils for an $n\times n$ rational matrix function $G(\lambda)$ for computing eigenvalues and poles of $G(\lambda)$ and show that the GF pencils are linearizations of $G(\lambda).$ These GF pencils generalize the GF ...

The DFOM method is an iterative method for computing the Drazin-inverse solution of consistent or inconsistent linear systems of the form Ax = b, where A ∈ źn n is a singular and in general non-Hermitian matrix that has an arbitrary index. This method ...

This paper proposes a consistency improving procedure for fuzzy preference relations. Fuzzy consistency index (FCI) and fuzzy consistency ratio (FCR) are developed to measure the consistency level for complete (or incomplete) fuzzy reciprocal preference ...

Random walks are a fundamental model in applied mathematics and are a common example of a Markov chain. The limiting stationary distribution of the Markov chain represents the fraction of the time spent in each state during the stochastic process. A ...

Experimental results in machine learning, data analysis and data mining often appear as comparisons between elements from a limited set. If a matrix of pairwise similarities is positively definite, then the set of elements is considered to be immersed ...

Eigenvalues and eigenvectors are one of the important topics over bipolar fuzzy linear algebra. In order to develop the bipolar fuzzy linear space we introduce in this article, the similarity relations, eigenvalues and eigenvectors of bipolar fuzzy ...

Efficiency, the basic concept of multi-objective optimization is investigated for the class of pairwise comparison matrices. A weight vector is called efficient if no alternative weight vector exists such that every pairwise ratio of the latter’s ...

Recent techniques of spectrum estimation focus on linear algebraic concept of subspaces. Spectral estimation based on noise subspace method has already been used for DNA sequence analysis having significance in genomic study. It is known that exons show ...

Online social networks are drawing attention to a large number of users who interact with each other to form positive and negative relations. Positive relations are produced from friendship, liking, trust whereas negative relations are outcome of ...