Abstract
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods.
Highlights
The past few years have witnessed a surge in research on embedding the vertices of a network into a low-dimensional, dense vector space
We summarize the key contributions of this work as follows: 1. We propose Neural-Brane, a custom neural network based model for learning node embedding vectors by integrating local topology structure and nodal attributes
The papers are classified into 6 categories according to its research domain, namely artificial intelligence (AI), database (DB), information retrieval (IR), machine learning (ML), human computer interaction (HCI), and multi-agent analysis
Summary
The past few years have witnessed a surge in research on embedding the vertices of a network into a low-dimensional, dense vector space. Most existing network embedding methods, including DeepWalk [15], LINE [18], Node2Vec [9], and SDNE [21], utilize the topological information of a network with the rationale that nodes with similar topological roles should be distributed closely in the learned low-dimensional vector space. While this suffices for node embedding of a bare-bone network, it is inadequate for most of today’s network datasets which include useful information beyond link connectivity. The existing embedding models do not provide a principled approach for incorporating nodal attributes into network embedding and fail to achieve the performance boost that may be obtained through
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