منابع مشابه
Attributed Social Network Embedding
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship n...
متن کاملAccelerated Attributed Network Embedding
Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. ...
متن کاملSigned Network Embedding in Social Media
Network embedding is to learn low-dimensional vector representations for nodes of a given social network, facilitating many tasks in social network analysis such as link prediction. The vast majority of existing embedding algorithms are designed for unsigned social networks or social networks with only positive links. However, networks in social media could have both positive and negative links...
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 کاملAligning Users across Social Networks Using Network Embedding
In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with “similar” followe...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2018
ISSN: 1041-4347,1558-2191,2326-3865
DOI: 10.1109/tkde.2018.2819980