Unevenness of Loop Location in Complex Networks

The loop structure plays an important role in many aspects of complex networks and attracts much attention. Among the previous works, Bianconi et al find that real networks often have fewer short loops as compared to random models. In this paper, we focus on the uneven location of loops which makes some parts of the network rich while some other parts sparse in loops. We propose a node removing process to analyze the unevenness and find rich loop cores can exist in many real networks such as neural networks and food web networks. Finally, an index is presented to quantify the unevenness of loop location in complex networks.Comment: 7 pages, 6 figure

Measuring Significance of Community Structure in Complex Networks

Many complex systems can be represented as networks and separating a network into communities could simplify the functional analysis considerably. Recently, many approaches have been proposed for finding communities, but none of them can evaluate the communities found are significant or trivial definitely. In this paper, we propose an index to evaluate the significance of communities in networks. The index is based on comparing the similarity between the original community structure in network and the community structure of the network after perturbed, and is defined by integrating all the similarities. Many artificial networks and real-world networks are tested. The results show that the index is independent from the size of network and the number of communities. Moreover, we find the clear communities always exist in social networks, but don't find significative communities in proteins interaction networks and metabolic networks.Comment: 6 pages, 4 figures, 1 tabl

A New Comparative Definition of Community and Corresponding Identifying Algorithm

In this paper, a new comparative definition for community in networks is proposed and the corresponding detecting algorithm is given. A community is defined as a set of nodes, which satisfy that each node's degree inside the community should not be smaller than the node's degree toward any other community. In the algorithm, the attractive force of a community to a node is defined as the connections between them. Then employing attractive force based self-organizing process, without any extra parameter, the best communities can be detected. Several artificial and real-world networks, including Zachary Karate club network and College football network are analyzed. The algorithm works well in detecting communities and it also gives a nice description for network division and group formation.Comment: 11 pages, 4 fihure

High expression of nucleophosmin is closely related to the grade and invasion of colorectal cancer

To explore the differential protein expression in the colorectal cancer (CRC) patients to validate a new biomarker for tumor progression. CRC tissues and their adjacent non-cancerous tissues were analyzed by two-dimensional LC/MS/MS. Nucleophosmin 1 (NPM1) was selected and confirmed its differential expression by Western blot. Immunohistological staining of NPM1 in tissues was performed to validate its correlation with clinicopathologic parameters of CRC patients. There were 39 candidates with significant difference between cancerous tissues and their adjacent non-cancerous tissues, which included 19 increased proteins and 20 decreased proteins in CRC samples. Especially, NPM1 was correlated with poor differentiation, and lymph node metastasis according to the analysis of patients’ clinicopathologic parameters. Increased expression of NPM1 can be as a critical biomarker for clinical diagnosis of tumor progression of CRC patients