Network-Based Document Clustering Using External Ranking Loss for Network Embedding

Abstract

Network-based document clustering involves forming clusters of documents based on their significance and relationship strength. This approach can be used with various types of metadata that express the significance of the documents and the relationships among them. In this study, we defined a probabilistic network graph for fine-grained document clustering and developed a probabilistic generative model and calculation method. Furthermore, a novel neural-network-based network embedding learning method was devised that considers the significance of a document based on its rankings with external measures, such as the download counts of relevant files, and reflects the relationship strength between the documents. By considering the significance of a document, reputative documents of clusters can be centralized and shown as representative documents for tasks such as data analysis and data representation. During evaluation tests, the proposed ranking-based network-embedding method performs significantly better on various algorithms, such as the k-means algorithm and common word/phrase-based clustering methods, than the existing network embedding approaches.INDEX TERMS Clustering algorithms, artificial neural networks.

Gated Multi-channel Network Embedding for Large-scale Mobile App Clustering

“…Some studies have investigated embedding nodes in graphs by extending text embedding methods [7,8,18]. Various types of graphs for social networks [1], scientific papers [4,5], protein interactions [8], drug-target interactions [19] and mobile apps [2,3] have been embedded to predict, cluster, or visualize nodes. Many previous approaches focus on the sampling strategy that is used to select target nodes for prediction.…”

“…PTE [20] is a semi-supervised embedding method that uses text networks instead of an unsupervised text embedding method such as skip grams. Yoon et al [3] also used a text network to embed a large number of mobile apps with a description set. In this study, we also use a text network as one of the basic channels for embedding nodes.…”

“…Network-based clustering is different from contentbased clustering in that it clusters documents based on connectivity. The network-based approach has performed better than content-based clustering in certain tasks [20,3] because it uses human-labeled information such as citations and tagging information. Academic papers with citations [4,5], web documents with hypertext [25,28], social networks [29], and news [30] with text networks have been clustered using the network-based approach.…”

“…Network embedding or graph embedding refers to the task of embedding a node in a network into a low-dimensional vector space. All nodes from a network such as a social network [1], mobile application (app) network [2,3], or scientific paper network [4,5] can be embedded through explicit or indirect connections. Embedding nodes enables similarities between nodes to be calculated and can be used for a variety of tasks such as node labeling, clustering, link prediction, visualization, and graph analysis [1].…”

“…Network-based clustering is one of the methods that groups nodes quickly and effectively by analyzing the importance of nodes and the strength of relationships between them based on graphs. Network-based clustering methods generally show higher performance because nodes are treated as connected objects rather than independently [3]. In recent years, research that applies the word2vec algorithm [6], which is widely used for word embedding, to network embedding has become mainstream.…”