Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other individuals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real-world applications, each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an approach to single view network embedding (SVNE) using graph factorization clustering (GFC) to the multi-view setting using an objective function that maximizes the agreement between views based on both the local and global structure of the underlying multi-view graph. Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks. Our experiments with several multi-view networks show that MVNE substantially outperforms the single view methods on integrated view and the state-of-the-art multi-view methods. We further show that even when the goal is to predict labels of nodes within a single target view, MVNE outperforms its single-view counterpart suggesting that the MVNE is able to extract the information that is useful for labeling nodes in the target view from the all of the views.
Network embedding methods aim to produce information preserving low-dimensional embeddings of nodes in large networks. State-of-the-art network embedding methods include Deepwalk @cite_3 , LINE @cite_40 and node2vec @cite_18 are limited to single view networks, i.e, networks with a single type of links. However, most real-world networks are comprised of multiple types of nodes and links @cite_25 @cite_40 @cite_38 wherein each type of link induces a view. Hence, there is a growing interest in network embedding methods for multi-view networks @cite_11 @cite_29 @cite_39 @cite_15 . Some multi-view network embedding methods use canonical correlation analysis (CCA) @cite_9 @cite_2 @cite_8 to integrate information from multiple views. Others construct multi-view embeddings by integrating embeddings obtained from the individual views. Examples include MVWE @cite_14 which uses a weighted voting mechanism to combine information from multiple views; MVE2vec @cite_21 which attempts to balance the preservation of unique information provided by specific views against information that is shared by multiple views; and DMNE @cite_28 which uses a co-regularized cost function to combine information from different views. MVWE, MVE2vec, and DMNE use deep neural network models at their core. Specifically, MVWE and MVE2vec are based on a skip-gram model and DMNE is based on an AutoEncoder.
@cite_38 @cite_18 @cite_14 @cite_8 @cite_28 @cite_29 @cite_9 @cite_21 @cite_3 @cite_39 @cite_40 @cite_2 @cite_15 @cite_25 @cite_11
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Network embedding aims to learn a low-dimensional vector representation (or embedding) for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, related multiple networks (e.g., social networks from different platforms) may contain complementary information which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible, which allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function to manipulate cross-network relationships. With multiple layers of non-linear mappings, DMNE progressively transforms each input network into a highly non-linear latent space, and in the meantime, adapts different latent spaces to each other through a co-regularized learning schema. Extensive experimental results on four real-life datasets demonstrate the effectiveness of our method. Network Embedding, which represents nodes in networks with efficient low-dimensional vectors, has been proved useful in a variety of applications. However, most existing approaches study single-view networks but not the multi-view networks with multiple types of relationships between nodes. Meanwhile, they ignore the rich features associated with the nodes, which is common in real world. In this paper, we propose a novel network embedding method, Intra-view and Inter-view attention for Multi-view Network Embedding (I2MNE), which leverages both the multi-view network structure and the node features to efficiently generate node representations. Specially, we introduce the intra-view attention when aggregating node features from neighbors for each single view and the inter-view attention when integrating representations across different views. Experiments on two real-world networks show that our approach outperforms other counterpart network embedding methods. Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization. Network embedding task aims at learning low-dimension latent representations of vertices while preserving the structure of a network simultaneously. Most existing network embedding methods mainly focus on static networks, which extract and condense the network information without temporal information. However, in the real world, networks keep evolving, where the linkage states between the same vertex pairs at consequential timestamps have very close correlations. In this paper, we propose to study the network embedding problem and focus on modeling the linkage evolution in the dynamic network setting. To address this problem, we propose a deep dynamic network embedding method. More specifically, the method utilizes the historical information obtained from the network snapshots at past timestamps to learn latent representations of the future network. In the proposed embedding method, the objective function is carefully designed to incorporate both the network internal and network dynamic transition structures. Extensive empirical experiments prove the effectiveness of the proposed model on various categories of real-world networks, including a human contact network, a bibliographic network, and e-mail networks. Furthermore, the experimental results also demonstrate the significant advantages of the method compared with both the state-of-the-art embedding techniques and several existing baseline methods.