The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collabor...

Bipartite projections have become a common way to measure spatial networks. They are now used in many subfields of geography, and among the most ways world city network, where intercity links inferred from firm co-location patterns. attractive because network can be indirectly readily available data. However, bipartite difficult analyze these networks weighted, larger weights do not necessarily...

We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new method to solve it from the viewpoint of distance metric learning. The method involves the learning of two mappings of the heterogeneous objects to a unified Euclidean space representing the network topology of the bipartite graph, where the graph is easy to infer. The algorithm can be form...

In this paper, we propose and study a simple bipartite network model. Significantly, we prove that the degree distribution of one kind nodes obey power-law form with adjustable exponents. And the other obeys exponential distribution. Furthermore, we study the degree-degree correlation of the model by calculating the mixing coefficient and find that the network model is dissortative. Numerical s...

In this paper, network flow algorithms for bipartite networks are studied. A network G = (V. E) is called bipartite if its vertex set V can be partitioned into two subsets V1 and V2 such that all edges have one endpoint in VY and the other in V2. Let n = I., nl = ll I1, n = IV21, m = El and assume without loss of generality that n s < n2. A bipartite network is called unbalanced if n << n and b...