Pitman-Yor process mixture model for community structure exploration considering latent interaction patterns

The statistical model for community detection is a promising research area in network analysis.Most existing statistical models of community detection are designed for networks with a known type of community structure,but in many practical situations,the types of community structures are unknown.To cope with unknown community structures,diverse types should be considered in one model.We propose a model that incorporates the latent interaction pattern,which is regarded as the basis of constructions of diverse community structures by us.The interaction pattern can parameterize various types of community structures in one model.A collapsed Gibbs sampling inference is proposed to estimate the community assignments and other hyper-parameters.With the Pitman-Yor process as a prior,our model can automatically detect the numbers and sizes of communities without a known type of community structure beforehand.Via Bayesian inference,our model can detect some hidden interaction patterns that offer extra information for network analysis.Experiments on networks with diverse community structures demonstrate that our model outperforms four state-of-the-art models.

Exploring the interaction patterns among taxa and environments from marine metagenomic data

The sequencing revolution driven by high-throughput technologies has generated a huge amount of marine microbial sequences which hide the interaction patterns among microbial species and environment factors. Exploring these patterns is helpful for exploiting the marine resources. In this paper, we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in spring, summer, fall and winter seasons. With the 16S rRNA pyrosequencing data of 76 time point taken monthly over 6 years, we first use our MtHc clustering algorithm to generate the operational taxonomic units （OTUs）. Then, employ the k-means method to divide 76 time point samples into four seasonal groups, and utilize mutual information （MI） to construct the four correlation networks among microbial species and environment factors. Finally, we adopt the symmetrical non-negative matrix factorization method to detect the interaction patterns, and analysis the relationship between marine species and environment factors. The results show that the four seasonal microbial interaction networks have the characters of complex networks, and interaction patterns are related with the seasonal variability; the same environmental factor influences different species in the four seasons; the four environmental factors of day length, photosynthetically active radiation, NO2＋ NO3 and silicate may have stronger influences on microbes than other environment factors.

基于交互模式的隐私保护方法研究(英文)

In network environments,before meaningful interactions can begin,trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy.Consequently,privacy protection and trust establishment become important in network interactions.In order to protect privacy while facilitating effective interactions,we propose a trust-based privacy protection method.Our main contributions in this paper are as follows:(1)We introduce a novel concept of k-sensitive privacy as a measure to assess the potential threat of inferring privacy;(2)According to trust and k-sensitive privacy evaluation,our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat;(3)By considering interaction patterns for privacy protection,our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate.Simulation results show that our method can achieve effective interactions with less privacy loss.

Temporal patterns of algae in different urban lakes and their correlations with environmental variables in Xi’an,China

Urban lakes were critical in aquatic ecology environments,but how environmental factors affected the distribution and change characteristics of algal communities in urban lakes of Xi’an city was not clearly.Here,we investigated the algal community structure of six urban lakes in Xi’an and evaluated the effects of water quality parameters on algae.The results indicated that the significant differences on physicochemical parameters existed in different urban lakes.The maximum concentration of total phosphorus in urban lakes was(0.18±0.01)mg/L and there was a phenomenon of phosphorus limitation.In addition,51 genera of algae were identified and Chlorella sp.was the dominant algal species,which was affiliated with Chlorophyta.Network analysis elucidated that each lake had a unique algal community network and the positive correlation was dominant in the interaction between algae species,illustrating that mature microbial communities existed or occupied similar niches.Redundancy analysis illustrated that environmental factors explained 47.35% variance of algal species-water quality correlation collectively,indicating that water quality conditions had a significant influence on the temporal variations of algae.Structural equation model further verified that algal community structure was directly or indirectly regulated by different water quality conditions.Our study shows that temporal patterns of algal communities can reveal the dynamics and interactions of different urban ecosystem types,providing a theoretical basis for assessing eutrophication levels and for water quality management.