One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hid...

We analyze the variation of prices in a model of an exchange market introduced by Kakade et al. [11], in which buyers and sellers are represented by vertices of a bipartite graph and trade is allowed only between neighbors. In this model the graph is generated probabilistically, and each buyer is connected via preferential attachment to v sellers. We show that even though the tail of the degree...

In this paper, we give method for probabilistic assignment to the Realistic Abduc-tive Reasoning Model, proposed in 1]. The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning the probability to the explanation...

The task of extracting the maximal amount of information from a biological network has drawn much attention from researchers, for example, predicting the function of a protein from a protein-protein interaction (PPI) network. It is well known that biological networks consist of modules/communities, a set of nodes that are more densely inter-connected among themselves than with the rest of the n...

For many parasites, the full set of hosts that are susceptible to infection is not known, and this could lead to a bias in estimates of transmission. We used counts of individual adult parasites from historical parasitology studies in southern Africa to map a bipartite network of the nematode parasites of herbivore hosts that occur in Botswana. Bipartite networks are used in community ecology t...

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to capability handling nonconvex formulations. However, fixed computation structure existing deep neural networks (DNNs) lacks flexibility with respect system size, i.e., number antennas or users. This paper develops a bipartite graph netwo...

We study the growth of bipartite networks in which the number of nodes in one of the partitions is kept fixed while the other partition is allowed to grow. We study random and preferential attachment as well as combination of both. We derive the exact analytical expression for the degree-distribution of all these different types of attachments while assuming that edges are incorporated sequenti...

A Simultaneous Parametric Maximum-flow (SPM) algorithm finds the maximum-flow and a min-cut of a bipartite parametric maximum-flow network simultaneously for all values of parameter . λ Instead of working with the original parametric network, a new network is derived from the original and the SPM gives a particular state of the flows in the derived network, from which the nested min-cuts under ...