Complex Network Point Of Strength And Its Complex Network Of Applied Research In The Biological Field

Complex networks that is short of a highly complexity network. As the following list of possible complications illustrates: (1) Structural complexity: the wiring diagram could be an intricate tangle(2) Network evolution: the wiring diagram could change over time. (3) Connection diversity: the links between nodes could have different weights, directions and signs. (4) Dynamical complexity: the nodes could be nonlinear dynamical systems.(5) Node diversity: there could be many different kinds of nodes.(6) Meta-complication: the various complications can influence each other.The comple～x network can describe variety different practical systems. Decades, scientists in various fields empirical studies a large number of the real world complex network systems. Here, we focus on the interest in the empirical statistical studies of so-called "cooperation - competition" weighted networks, and suggested that each actor from each of cooperation - competition act got resources is defined as the node's weight. We propose define actor's cooperation or competition relation in the acts as edges In this way, one act can be described by a complete graph composed by the actors where every pair of them are connected with edges. Such a network may not be a social network, it can belong to many other kinds of networks too, but because the topological structure has common characteristics, their statistics properties have common characteristics. In our studied the actual networks ,we found that in 2004 Athens Olympic Games network, IT products selling network, mixed drinks network, university independent recruitment network, protein domain interaction network can be described by the networks.We have researched social and non- social system: First of all, as losing some information of the traditional projection the bipartite graph method. To better retain the bipartite graph information, Zhou et al. proposed a resource-allocation projection method. Under this average resource-allocation assumption, the bipartite graph projection method can be applied in the networks where actually resource information is unavailable. Secondly, to better describe the real world system, we propose investigating some competing bipartite networks where the resource information is available. Then, we defined the node strength as the total competition influence of actor to its neighbors. Relying on 2004 Athens Olympic Game network,IT products selling network, mixed drinks network, university independent recruitment network, to discuss the relation of node strength connectivity correlation with node degree,multiple edge node strength et al. to further demonstrates the superiority of the resource allocation method.Interactions between protein domains can be described by the network. At present, the complex network applications research in biological has inspired many complex networks, as well as researchers in the field of biological interest.The network structures of complex systems are believed to be the results of evolution. In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship among biological organisms. For this purpose, firstly, we constructed Pfam domain interaction network for each of the 470 completely sequenced organisms, therefore each organism was correlated with a specific Pfam domain interaction network; secondly, we inferred the evolutionary relationship of these organisms with nearest neighbor joining method; thirdly, the evolutionary relationship among organisms constructed in the second step was used as the evolutionary course of Pfam domain interaction network constructed in the first step. Our analysis about this evolutionary course shows: (i) there is a conserved sub-network structure in network evolution. In this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as hub by attached preferentially with new added nodes; (ii) few nodes are conserved as hubs, most of the other nodes are conserved as the one with very low degree; (iii) in the course of network evolution, new nodes were added to the network either individually in most cases or as clusters with relative high clustering coefficient in very few cases. We focus on cooperation - competition networks research and its applications in biological, empirical and statistical analysis of several real systems, from which a number of interesting results were obtained. The research is rough. However, we expect as a beginning, the work could inspire people to research the cooperation - competition networks and biological networks deeply.