Applying latent dirichlet allocation to group discovery in large graphs

This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (such as Infinite Relational Models) have only been applied to small graphs with a couple of hundred nodes. LDA-G (short for Latent Dirichlet Allocation for Graphs) utilizes a well-known topic modeling algorithm to find latent group structure. Specifically, we modify Latent Dirichlet Allocation (LDA) to operate on graph data instead of text corpora. Our modifications reflect the differences between real-world graph data and text corpora (e.g., a node’s neighbor count vs. a document’s word count). In our empirical study, we apply LDA-G to several large graphs (with thousands of nodes) from PubMed (a scientific publication repository). We compare LDA-G’s quantitative performance on link prediction with two existing approaches: one Bayesian (namely, Infinite Relational Model) and one non-Bayesian (name...

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Keith Henderson, Tina Eliassi-Rad
• Keywords: Applied Computing, Graph Data, Latent Dirichlet Allocation, Latent Group Structure, SAC 2009