… To date, while many network embedding methods … embedding vectors. In order to model
pairwise topic relevance between linked text nodes, we propose topical network embedding, …

… schema inspires us to embed both users and … ’s embedding vector to topic embedding
vectors, we can easily measure affinity between a user and a topic, and thus capture users’ topical …

… a baseline method because this topical network embedding model also considers the topic
… , and latent topics for learning topical network embeddings. Note that TNE adopts random …

… basic word embedding representation and allow the resulting topical word embeddings to
model … As compared to multi-prototype word embedding models which build multiprototypes of …

… This paper presents Topical, a novel deep neural network for repository level embeddings.
… aggregation techniques, are outperformed by Topical’s utilization of an attention mechanism. …

… Using these word networks, ClusTop is then able to automatically … embedding techniques
in constructing the word network graph, and utilizes edge weights based on word embedding. …

… In Section 5.3, we evaluate the the performance LTE through a task of topical word … topics
and the embedding generated by LTE are effective for identifying topical words of documents. …

… Specifically, it captures papers’ citation proximity, authors’ collaboration proximity, venues’
information, labeled information, and topical relevance to generate personalized paper …

… This study proposes a framework of identifying topic evolutionary pathways based on
network analytics: Firstly, keyword networks are constructed, in which a piecewise linear …

… the embedding space. e contiguity of the embedding space enables us to capture topical
co-occurrence pa erns conveniently—we further embed documents into the same vector space, …