We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results.
Some previous studies embedded vectors to the elements. Among them, large-scale information network embedding (LINE) @cite_1 embedded a vector to each node in information network. LINE handles single type of information and prepares a network for each element separately. By contrast, our model simultaneously handles all the types of information.
@cite_1
1888005072 2536880093 2530041791 2788816357
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online https: github.com tangjianpku LINE . The problem of representing large-scale networks with low-dimensional vectors has received considerable attention in recent years. Except the networks that include only vertices and edges, a variety of networks contain information about groups or communities. For example, on Facebook, in addition to users and the follower-followee relations between them, users can also create and join groups. However, previous studies have rarely utilized this valuable information to generate embeddings of vertices. In this paper, we investigate a novel method for learning the network embeddings with valuable group information for large-scale networks. The proposed methods take both the inner structures of the groups and the information across groups into consideration. Experimental results demonstrate that the embeddings generated by the proposed methods significantly outperform state-of-the-art network embedding methods on two different scale real-world network This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods rely solely on the network structure, i.e., the linkage relationships between nodes, but ignore the rich content information associated with it, which is common in real world networks and beneficial to describing the characteristics of a node. In this paper, we propose content-enhanced network embedding (CENE), which is capable of jointly leveraging the network structure and the content information. Our approach integrates text modeling and structure modeling in a general framework by treating the content information as a special kind of node. Experiments on several real world net- works with application to node classification show that our models outperform all existing network embedding methods, demonstrating the merits of content information and joint learning. 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.