Topic Model for Graph Mining

Abstract

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

A Bayesian nonparametric model for multi-label learning

“…This idea is similar with the subsequent topic models (Blei et al 2003;Xuan et al 2015a) that are Bayesian models with fixed-dimensional probability distributions. They are originally designed for unsupervised text mining task which aims to discover hidden topics (i.e., word distributions) in the text corpus.…”

Explicitly and implicitly exploiting the hierarchical structure for mining website interests on news events

“…For example, the labels of document [5], time of documents [34], authors of documents [33], emotions of documents [3,32], and so on. There are also some works trying to release the independent of documents and discovered topics by considering the citation relations between documents [7,27], relations of words [36], and relations of topics [4]. However, all these works are still based on 'bag-of-words' assumption and the relations of keywords within documents are ignored.…”

Incremental learning from news events

“…Newman et al [17] proposed to use CorrLDA1 and CorrLDA2 to model a correlation between topics and entities. Xuan et al [34] proposed a new topic model for graph mining. Compared with these previous work, our event entity topic model EETM associate each topic with additional information of entity distribution and entity type distribution.…”