struc2gauss: Structural role preserving network embedding via Gaussian embedding
نویسندگان
چکیده
منابع مشابه
Community Preserving Network Embedding
Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the firstand second-order proximities of n...
متن کاملWord Representations via Gaussian Embedding
Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision...
متن کاملUser Profile Preserving Social Network Embedding
This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent lowdimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in ...
متن کاملTree preserving embedding
The goal of dimensionality reduction is to embed high-dimensional data in a low-dimensional space while preserving structure in the data relevant to exploratory data analysis such as clusters. However, existing dimensionality reduction methods often either fail to separate clusters due to the crowding problem or can only separate clusters at a single resolution. We develop a new approach to dim...
متن کاملSPINE: Structural Identity Preserved Inductive Network Embedding
Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the firstand secondorder proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant fro...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2020
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-020-00684-x