Genetic diversity, population structure, and DNA fingerprinting of Ailanthus altissima var. erythrocarpa based on EST-SSR markers.

Ailanthus altissima var. erythrocarpa is an A. altissima variety with high economic, ecological and ornamental value, but there have been no reports on the development of SSR primers for it. According to the SSR primer information provided by the transcriptome of A. altissima var. erythrocarpa, 120 individuals with different redness levels were used to screen polymorphic primers. Transcriptomic analysis revealed 10,681 SSR loci, of which mononucleotide repeats were dominant (58.3%), followed by dinucleotide and trinucleotide repeats (16.6%, 15.1%) and pentanucleotide repeats (0.2%). Among 140 pairs of randomly selected primers, nineteen pairs of core primers with high polymorphism were obtained. The average number of alleles (Na), average number of effective alleles (Ne), average Shannon's diversity index (I), average observed heterozygosity (Ho), average expected heterozygosity (He), fixation index (F) and polymorphic information content (PIC) were 11.623, 4.098, 1.626, 0.516, 0.696, 0.232 and 0.671, respectively. Nineteen EST-SSR markers were used to study the genetic diversity and population structure of A. altissima var. erythrocarpa. The phylogenetic tree, PCoA, and structure analysis all divided the tested resources into two categories, clearly showing the genetic variation between individuals. The population showed high genetic diversity, mainly derived from intraspecific variation. Among nineteen pairs of primers, 4 pairs (p33, p15, p46, p92) could effectively distinguish and be used for fingerprinting of the tested materials. This study is of great significance for genetic diversity analysis and molecular-assisted breeding of A. altissima var. erythrocarpa.

Attribute Augmented Network Embedding Based on Generative Adversarial Nets.

Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applications, learning these representations for networks with attribute information is difficult due to the heterogeneity between structure and attribute information. Many existing methods have been proposed to preserve explicit proximities between nodes, with optimization limited to node pairs with large structure and attribute proximities, which may lead to overfitting. To address the above problems, we adopt an attribute augmented network to represent attribute and structure information in a unified framework. Specifically, we study the problem of attribute augmented network embedding that exploits the strength of generative adversarial nets (ANGANs) in capturing the latent distribution of data to learn robust and informative representations of nodes. The ANGAN method obtains the low-dimensional representations of nodes through adversarial learning between the generative and discriminative models. The generative model approximates the underlying connectivity and attributes distributions of nodes by using the distributions generated from the learned representations. It is implemented by utilizing the properties of the attribute augmented network to improve the traditional Skip-gram model. The discriminative model is designed as a binary classifier to distinguish the truly connected node pairs from the generated ones. The pre-training algorithm and the teacher forcing approach are adopted to improve training efficiency and stability. Empirical results show that ANGAN generally outperforms state-of-the-art methods in various real-world applications, which demonstrates the effectiveness and generality of our method.

JORA: Weakly Supervised User Identity Linkage via Jointly Learning to Represent and Align.

The user identity linkage that establishes correspondence between users across networks is a fundamental issue in various social network applications. Efforts have recently been devoted to introducing network embedding techniques that map the different network users into the common representation space, thereby inferring user correspondence based on the similarities of their representations. However, existing studies that separately train the network embedding and space alignment in two stages may lead to conflict between the objectives of the two stages. Besides, the similarities between unlabeled cross-network user pairs are difficult to define and largely impact the result. Moreover, many previous methods still need plenty of labeled aligned user pairs to ensure performance, which may not be available. To address the above problems, we propose to solve the weakly-supervised user identity linkage problem via JOintly learning to Represent and Align, i.e., the JORA model. The architecture of JORA adopts the inductive graph convolutional network (GCN) that learns representations for each network. The model is jointly optimized by the representation learning component and alignment learning component. The former one aims to preserve the similarities between intranetwork users. The latter one aligns the different spaces by a projection function and aims to preserve the similarities between cross-network users. A specific attention mechanism is proposed to learn self-adaptive similarities for unlabeled user pairs during alignment learning and it reduces the error propagation caused by predefined similarities. The joint optimization helps perceive network characteristics during alignment and reduces the number of labeled users required. Experiments conducted on real social networks show that the proposed model achieves significantly better performance than the state-of-the-art methods.

CAMU: Cycle-Consistent Adversarial Mapping Model for User Alignment Across Social Networks.

The user alignment problem that establishes a correspondence between users across networks is a fundamental issue in various social network analyses and applications. Since symbolic representations of users suffer from sparsity and noise when computing their cross-network similarities, the state-of-the-art methods embed users into the low-dimensional representation space, where their features are preserved and establish user correspondence based on the similarities of their low-dimensional embeddings. Many embedding-based methods try to align latent spaces of two networks by learning a mapping function before computing similarities. However, most of them learn the mapping function largely based on the limited labeled aligned user pairs and ignore the distribution discrepancy of user representations from different networks, which may lead to the overfitting problem and affect the performance. To address the above problems, we propose a cycle-consistent adversarial mapping model to establish user correspondence across social networks. The model learns mapping functions across the latent representation spaces, and the representation distribution discrepancy is addressed through the adversarial training between the mapping functions and the discriminators as well as the cycle-consistency training. Besides, the proposed model utilizes both labeled and unlabeled users in the training process, which may alleviate the overfitting problem and reduce the number of labeled users required. Results of extensive experiments demonstrate the effectiveness of the proposed model on user alignment on real social networks.

Multimodal Deep Network Embedding With Integrated Structure and Attribute Information.

Network embedding is the process of learning low-dimensional representations for nodes in a network while preserving node features. Existing studies only leverage network structure information and emphasize the preservation of structural features. However, nodes in real-world networks often have a rich set of attributes providing extra semantic information. It has been demonstrated that both structural and attribute features are important for network analysis tasks. To preserve both features, we investigate the problem of integrating structure and attribute information to perform network embedding and propose a multimodal deep network embedding (MDNE) method. MDNE captures the non-linear network structures and the complex interactions among structures and attributes using a deep model consisting of multiple layers of non-linear functions. Since structures and attributes are two different types of information, a multimodal learning method is adopted to pre-process them and help the model to better capture the correlations between node structure and attribute information. We define the loss function employing structural and attribute proximities to preserve the respective features, and the representations are obtained by minimizing the loss function. Results of extensive experiments on four real-world data sets show that the proposed method performs significantly better than baselines on a variety of tasks, which demonstrates the effectiveness and generality of our method.