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Gut microbiomes play an essential role in host survival and local adaptation and thus can facilitate the invasion of host species. Biological invasions have been shown to be linked to the genetic properties of alien host species. It is thus plausible that the holobiont, the host, and its associated microbiome act as an entity to drive invasion success. The bighead carp and silver carp (bigheaded carps), invasive species that exhibit extensive hybridization in the Mississippi River Basin (MRB), provided a unique model to test the holobiont hypothesis of invasion. Here, we investigated the microbiomes of foreguts and hindguts in bigheaded carps and their reciprocal hybrids reared in aquaculture ponds using 16S amplicons and the associated gene prediction. We found an admixed pattern in the gut microbiome community in bigheaded carp hybrids. The hybrid gut microbiomes showed special characteristics such as relatively high alpha diversity in the foregut, an increasing dissimilarity between foreguts and hindguts, and a remarkable proportion of genes coding for putative enzymes related to their digestion of main food resources (Cyanobacteria, cellulose, and chitin). The pond‐reared hybrids had advantageous features in genes coding for putative enzymes related to their diet. The above results collectively suggested that the gut microbiomes of hybrids could be beneficial to their local adaptation (e.g., food resource utilization), which might have facilitated their invasion in the MRB. The gut microbial findings, along with the intrinsic genomic features likely associated with life‐history traits revealed in our recent study, provide preliminary evidence supporting the holobiont hypothesis of invasion.

As a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional spaces, has a variety of applications on complex networks. Most current methods and models are not suitable for bipartite networks, which have two different types of nodes and there are no links between nodes of the same type. Furthermore, the only existing methods for bipartite network embedding ignore the internal mechanism and highly nonlinear structures of links. Therefore, in this paper, we propose a new deep learning method to learn the node embedding for bipartite networks based on the widely used autoencoder framework. Moreover, we carefully devise a node-level triplet including two types of nodes to assign the embedding by integrating the local and global structures. Meanwhile, we apply the variational autoencoder (VAE), a deep generation model with natural advantages in data generation and reconstruction, to enhance the node embedding for the highly nonlinear relationships between nodes and complex features. Experiments on some widely used datasets show the effectiveness of the proposed model and corresponding algorithm compared with some baseline network (and bipartite) embedding techniques.

Major histocompatibility complex (MHC) class I molecules play a crucial role in the immune response by binding and presenting pathogen-derived peptides to specific CD8+ T cells. From cDNA of 20 individuals of wild grass carp (Ctenopharyngodon idellus), we could amplify one or two alleles each of classical MHC class I genes Ctid-UAA and Ctid-UBA. In total, 27 and 22 unique alleles of Ctid-UAA and Ctid-UBA were found. The leader, α1, transmembrane and cytoplasmic regions distinguish between Ctid-UAA and Ctid-UBA, and their encoded α1 domain sequences belong to the ancient lineages α1-V and α1-II, respectively, which separated several hundred million years ago. However, Ctid-UAA and Ctid-UBA share allelic lineage variation in their α2 and α3 sequences, in a pattern suggestive of past interlocus recombination events that transferred α2+α3 fragments. The allelic Ctid-UAA and Ctid-UBA variation involves ancient variation between domain lineages α2-I and α2-II, which in the present study was dated back to before the ancestral separation of teleost fish and spotted gar (> 300 million years ago). This is the first report with compelling evidence that recombination events combining different ancient α1 and α2 domain lineages had a major impact on the allelic variation of two different classical MHC class I genes within the same species.

Exploring and understanding the temporal structure of dynamic networks attract extensive attention over the past few years. Most of these current research focuses on temporal community detection, evolution analysis or link prediction from a mission-oriented perspective. In fact, these three tasks should be not isolated but mutually reinforcing. Transforming these three tasks into a unified framework, it is crucial to extract the evolution pattern, which helps to understand the time-varying characteristics of temporal structure in essence. In addition, to the best of our knowledge, there is no work focusing on modeling and uncovering the local and global evolution pattern hidden in temporal community structure, simultaneously. In this paper, we propose a novel framework based on Orthogonal Nonnegative Matrix Factorization to Explore the Evolution Pattern (ONMF-EEP) for analyzing and predicting the time-varying structures in dynamic networks from local and global perspectives. The nature of this framework assumes that community structures are subject to a local evolution pattern (LEP) at each snapshot, and these LEPs are from a common global evolution pattern (GEP). The framework can synchronously detect temporal community structure, extract evolution pattern, and predict structure including communities and future snapshot links. The extensive experiments on real-world networks and artificial networks demonstrate that our proposed framework is highly effective on the tasks of dynamic network analysis.

In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling temporal community structures. But the number of communities cannot be determined automatically. Therefore, a model, Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF), is proposed in this paper. It focuses on modeling the temporal community structures with evolution characteristics. More specifically, the evolution behavior, which is introduced into EvoBNMF, can quantify the transfer intensity of communities between adjacent snapshots for modeling the evolution characteristics. Innovatively, the most appropriate number of communities can be determined autonomously by shrinking the corresponding evolution behaviors. Experimental results show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.

With the fast development of information network, the scale of social network has become very significant, and it has become more difficult to obtain the information of entire network. In addition, because current mining method for complicated network community utilizes the information of node link or property, which cannot effectively detect the community with dense member links and highly similar properties. As a result, most current algorithms are impractical for online social network with large scale, and we propose a community detection algorithm for multi-layer social network based on local random walk (MRLCD); this algorithm determines the core node based on the repeatability of multi-layer nodes. It expands from a core node, has local random walk in multi-layer network, identifies and controls the random walk scope of node based on the intra-layer and interlayer trust. During the walk process, the clustering coefficient of nodes to be combined is comprehensively compared to further complete a local community search, and the optimal local community search is obtained through multiple iterations. Finally, the multi-layer modularity is used as the indicator for measurement and evaluation of algorithm performance, and its performance is compared with other network clustering algorithms such as GL, LART and PMM through four actual multi-layer network datasets. The MRLCD algorithm can autonomously explore the local community structure of given node, and effectively improve the stability and accuracy for local community detection in multi-layer social network.

Community structure analysis in dynamic network is widely concerned in various fields, which mainly focus on temporal community detection and community evolution analysis. Most of the related works usually fist detect communities and then analyze evolution. This leads to a loss of evolution information on temporal community detection because block structures and evolution characteristics coexist in dynamic networks. Even thought a few modelbased approaches consider the evolution characteristics into community detection, they need to know the number of communities in advance and ignore automatic determination of the number of communities, which is a model selection problem. In the paper, we propose an model, Evolutionary Bayesian Non-negative Matrix Factorization (EvoBNMF), to model community structures with evolution characteristics for boosting the performance of temporal community detection. In detail, EvoBNMF introduces evolution behaviors, which quantify the transition relationships of communities between adjacent snapshots, to describe the evolution characteristics of community structure. Innovatively, EvoBNMF can catch the most appropriate number of communities autonomously by shrinking the corresponding evolution behaviors. Experimental results from synthetic networks and real-world networks over several state-of-the-art methods show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.

Link prediction in bipartite networks is attracting tremendous research interests. Most previous studies mainly assume the generation of link follows a predefined prior mechanism while neglecting complexity of the link generation mechanisms. To address this limitation, we present a parameter-free method, termed Structural Enhancement and Structural Perturbation (SESP), which jointly exploits explicit relations (low-order information) and implicit relations (high-order information) from the perspective of perturbation. The essence of SESP is that it transforms bipartite link prediction into monopartite link prediction without losing any information and predicts the missing links from a perturbed perspective. Compared with traditional link prediction methods, SESP does not assume a particular link generation mechanism, but learns this mechanism from the network itself. Extensive experiments on several disparate real-world bipartite networks demonstrate the effectiveness of the SESP model.

Link prediction attracts the attention of a large number of researchers due to the extensive application in social and economic fields. Many algorithms have been proposed in recent years. They show good performance because of having own particularly selected networks. However, on the other networks, they do not necessarily have good universality. Moreover, there are no other methods to evaluate the performance of new algorithm except AUC and precision. Therefore, we cannot help questioning this phenomenon. Can it really reflect the performance of an algorithm? Which attributes of a network have great influence on the prediction effect? In this paper, we analyze 21 real networks by multivariate statistical analysis. On the one hand, we find that the heterogeneity of network plays a significant role in the result of link prediction. On the other hand, the selection of network is very essential when verifying the performance of new algorithm. In addition, a nonlinear regression model is produced by analyzing the relationship between network properties and similarity methods. Furthermore, 16 similarity methods are analyzed by means of the AUC. The results show that it is of great significance for the performance of a new algorithm to design the evaluation mechanism of classification.