Multi-View Network Representation Learning Algorithm Research

The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.

Multi-view Low-rank Preserving Embedding: A novel method for multi-view representation

In conventional unsupervised multi-view clustering (MVC), learning of representations from heterogeneous multiview data and its subsequent clustering are often separately optimized. The disparate optimization would lead to suboptimal performance because multi-view representation learning is not goal-directed. In this paper, we unify unsupervised multi-view learning and deep clustering in a novel discriminative Self-supervised Deep Correlational Multi-view Clustering (SDC-MVC) network. A new unified loss function is proposed to incorporate consensus information into discriminative representations, in which, the former is learnt by maximizing the canonical correlation among multi-view representations projected by neural networks, and the later is achieved through using confident clustering assignments as supervision. Further, multi-view representations are harnessed by our proposed Deep Serial Feature-level (DSF) Fusion layer. Experiments on three public datasets demonstrated that our method outperforms six state-of-the-art correlation-based MVC algorithms in terms of three evaluation metrics.

Recommender systems have been an efficient strategy to deal with information overload by producing personalized predictions. Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. Our model consists of two parts: the first part uses a fused model of deep neural network and matrix factorization to predict the criteria ratings and the second one employs a deep neural network to predict the overall rating. The experimental results on two datasets, including a real-world dataset, show that the proposed model outperformed several state-of-the-art methods across different datasets and performance evaluation metrics.

With the wide application of network data in many fields, network representation learning technology has become the focus of scholars' research. Especially in dynamic networks, how to combine network structure information with time information is the difficulty of current research. This paper proposes a dynamic network representation learning method (ESNA) based on temporal neighborhood aggregation. Further, We divide the influence of nodes on the network into local power and global power, learn the local influence of nodes on the network by using time difference attention mechanism aggregation in the temporal neighborhood, delimit the influence range of nodes on the global network by using random walk model outside the time series neighborhood, and finally fuse the local and global influence of nodes on the network based on word vector model to form a network embedded with time information. We experimented with real dynamic network data in the link prediction task, and the results showed the effectiveness of our method.

Network Representation Learning (NRL), which aims to embed nodes into a latent, low-dimensional vector space while preserving some network properties, facilitates the network analysis tasks. The goal of most NRL methods is to make similar nodes represented similarly in the embedding space. Many methods adopt the skip-gram model to achieve such goal by maximizing the predictive probability among the context nodes for each center node. The context nodes are usually determined based on the concept of \textit{proximity} which is defined based on some explicit network features. However, these proximities may result in a loss of training samples and have limited discriminative power. We propose a general and unified generative adversarial learning framework to address the problems. The proposed framework can handle almost all kinds of networks in a unified way, including homogeneous plain networks, attribute augmented networks and heterogeneous networks. It can improve the performances of the most of the state-of-the-art skip-gram based NRL methods. Moreover, another unified and general NRL method is extended from the framework. It can learn the network representation independently. Extensive experiments on proximity preserving evaluation and two network analysis tasks, i.e., link prediction and node classifications, demonstrate the superiority and versatility of our framework.

Network representation learning algorithms, which aim at automatically encoding graphs into low-dimensional vector representations with a variety of node similarity definitions, have a wide range of downstream applications. Most existing methods either have low accuracies in downstream tasks or a very limited application field, such as article classification in citation networks. In this paper, we propose a novel network representation method, named Link Prediction based Network Representation (LPNR), which generalizes the latest graph neural network and optimizes a carefully designed objective function that preserves linkage structures. LPNR can not only learn meaningful node representations that achieve competitive accuracy in node centrality measurement and community detection but also achieve high accuracy in the link prediction task. Experiments prove the effectiveness of LPNR on three real-world networks. With the mini-batch and fixed sampling strategy, LPNR can learn the embedding of large graphs in a few hours.

In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., Fa and Fb. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

Most existing network representation learning algorithms focus on network structures for learning. However, network structure is only one kind of view and feature for various networks, and it cannot fully reflect all characteristics of networks. In fact, network vertices usually contain rich text information, which can be well utilized to learn text-enhanced network representations. Meanwhile, Matrix-Forest Index (MFI) has shown its high effectiveness and stability in link prediction tasks compared with other algorithms of link prediction. Both MFI and Inductive Matrix Completion (IMC) are not well applied with algorithmic frameworks of typical representation learning methods. Therefore, we proposed a novel semi-supervised algorithm, tri-party deep network representation learning using inductive matrix completion (TDNR). Based on inductive matrix completion algorithm, TDNR incorporates text features, the link certainty degrees of existing edges and the future link probabilities of non-existing edges into network representations. The experimental results demonstrated that TFNR outperforms other baselines on three real-world datasets. The visualizations of TDNR show that proposed algorithm is more discriminative than other unsupervised approaches.

In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs' latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.

Network representation learning is receiving increasing attention from scholars. Among them, methods based on graph neural networks have become particularly popular. However, most existing methods currently only focus on networks with a single type of relations. In the real world, networks contain a wealth of diverse information, with multiple types of relationships between nodes. In this paper, we propose a graph neural network-based multiplex network representation learning model (GNMRL). We model nodes within each layer of the multiplex network by aggregating neighbor information. Additionally, since nodes are influenced by different network layers, we integrate node interaction information across network layers. We conducted systematic experiments on four datasets, and the results show that GNMRL outperforms other comparison methods in both link prediction and node classification tasks.

Evaluating disease-disease associations is critical for understanding the complex mechanism of less well-studied disease and facilitating disease diagnosis and therapy. Therefore, people present some methods to explore the potential connection between disease. However, they mostly rely on overlaps between disease-related gene sets or distances within ontological levels, where the diseases are described by discrete or sparse feature vectors failing to capture the latent semantic information of disease. In this study, inspired by the successfully application of network representation in bioinformatics tasks, we propose a novel method to evaluate disease-disease associations by combining network representation learning with best-match average similarity strategy. Our method can capture more semantically rich disease feature vectors and make better use of prior biological knowledge. The experimental results on a public benchmark dataset showed that our method can improve the predictive performance, and the visualization analysis further demonstrate the effectiveness of disease latent features learned by network representation learning.

DISL: Deep Isomorphic Substructure Learning for network representations

Numerous experiments have demonstrated that long non-coding RNA (lncRNA) play an important role in various systems of the human body. LncRNA deletions or mutations can cause human disease. The prediction of lncRNA-disease associations is conducive to the diagnosis and prevention of complex diseases. As we all know, it is a time-consuming and expensive process to predict lncRNA-disease associations via biological experiments. However, the computation methods can effectively discover lncRNA-disease associations with less human and material resources. In this paper, we propose a neural network-based matrix factorization model to predict lncRNA-disease associations, which is called NeuMFLDA. NeuMFLDA first converts the one-hot encoding of disease or lncRNA into word vector via the embedding layer. Then combined with the memorization of the conventional matrix factorization and the generalization of the multi-layer perceptron, the lncRNA-disease associations can be predicted more accurately. In addition, as opposed to conventional pointwise loss function, a new pairwise loss function is proposed to update our model parameters. Our new loss function optimizes the model from the perspective of ranking priority, which is more in line with the solution to the lncRNA-disease associations prediction task. Experiments show that NeuMFLDA reaches average AUCs of 0.904 ± 0.003 and 0.918 ± 0.002 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-stateof- art methods. In case studies, 9, 9 and 8 out of top-10 candidate lncRNAs are verified by recently published literatures for hepatocelluar carcinoma, kidney cancer and ovarian cancer, respectively. In short, NeuMFLDA is an effective tool for predicting lncRNA-disease associations.

In this paper, a revised recommendation system is constructed that ensembles deep neural network and matrix factorization under its framework and uses the explicit feedback for collaborative filtering. Recent works used deep neural network in recommendation for processing auxiliary attributes, but their interaction function is just an inner product on latent features of users and items. For modelling the recommendation system in this research, multi-layer perceptron was used to learn the interaction function. Experiments show significant decrease in MAE and RMSE to be 0.69 and 0.94 respectively, which is comparatively better than general collaborative filtering methods.