Bipartite networks refer to the networks created from affiliation relationships, such as patient-provider relationships in healthcare data. Analyzing such networks allows us to gain additional insights on groups that share the same member and members that belong to the same group. This paper develops methods to visualize bipartite network data in healthcare using the Annotate facility. Link ana...

MOTIVATION Many biological networks, including transcriptional regulation, metabolism, and the absorbance spectra of metabolite mixtures, can be represented in a bipartite fashion. Key to understanding these bipartite networks are the network architecture and governing source signals. Such information is often implicitly imbedded in the data. Here we develop a technique, network component mappi...

Network science is a powerful tool for analyzing complex systems in fields ranging from sociology to engineering to biology. This paper is focused on generative models of bipartite graphs, also known as twoway graphs. We propose two generative models that can be easily tuned to reproduce the characteristics of real-world networks, not just qualitatively, but quantitatively. The measurements we ...

The statistical analysis of the structure of bipartite ecological networks has increased in importance in recent years. Yet, both algorithms and software packages for the analysis of network structure focus on properties of unipartite networks. In response, we describe BiMAT, an objectoriented MATLAB package for the study of the structure of bipartite ecological networks. BiMAT can analyze the ...

This paper introduces a computationally inexpensive method of extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in one-mode projections are distributed according to a Poisson binomial distribution. Finding the expected weight distribution of a one-mode network projected from a random bipartite network only requires knowledge of the bip...

Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly ...

This paper shows that with B = {1, 2, . . . , n}, the smallest k such that (B ×B)− {(j, j) | j ∈ B} = k ⋃ i=1 (Ci ×Di) is s(n), where s(n) is the smallest integer k such that n 6 ( k b k 2 c ) . This provides a simple set-based formulation and a new proof of a result for boolean ranks [2] and biclique covering of bipartite graphs [1, 5], making these intricate results more accessible.

Social Networks may exist as bipartite graphs which evolve with new links being continuously added between the two disjoint set of nodes. To understand the dynamism of such bipartite networks, it is useful to predict links between existing nodes. Using the Yelp social network between users and businesses, we attempt to predict user reviews on businesses and the rating of predicted reviews. In o...

Although bipartite networks have been effective in identifying how significant biomarkers are associated to different subsets of patients, little is known about how domain experts use such patterns to infer biological pathways. Here we present a case study to elucidate how patterns from a bipartite network visualization were used in conjunction with Ingenuity Pathway Analysis (IPA) to infer pat...