2018 IEEE 12th International Conference on Semantic Computing (ICSC) (2018)
Laguna Hills, CA, USA
Jan 31, 2018 to Feb 2, 2018
Network embedding enables to apply off-the-shelf machine learning methods to the nodes on the network. Leveraging the textual information associated with nodes into network embedding methods is advantageous. However, only a few works try to leverage textual information to network embeddings. Moreover, the structure of networks and associated texts could be dynamically changed over time, this property leads shifts of the vector representation of nodes and words. However, to the best of our knowledge, none of previous network embedding methods considers chronological changes of vector representations of nodes and words. In this paper, we propose a dynamic text attribute network embedding method, which embeds the nodes and the words in a cooperative manner and takes chronological changes of vector representations of nodes and words into account. Experimental results show that (1) vector representations of nodes of our method achieve higher accuracy than baseline methods in classification and clustering tasks; (2) The vector representations of nodes and words successfully capture semantic similarity; And (3) our method successfully capture the chronological change of the vector representations over time.
learning (artificial intelligence), pattern classification, pattern clustering, text analysis, vectors
H. Ito, T. Komamizu, T. Amagasa and H. Kitagawa, "Network-Word Embedding for Dynamic Text Attributed Networks," 2018 IEEE 12th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2018, pp. 334-339.