Learning Shared Subgraphs in Ising Model Pairs

Probabilistic graphical models (PGMs) are effective for capturing the statistical dependencies in stochastic databases. In many domains (e.g., working with multimodal data), one faces multiple information layers that can be modeled by structurally similar PGMs. While learning the structures of PGMs in isolation is well-investigated, the algorithmic design and performance limits of learning from multiple coupled PGMs are investigated far less. This paper considers learning the structural similarities shared by a pair of Ising PGMs. The objective is learning the shared structure with no regard for the structures exclusive to either of the graphs, and significantly different from the existing approaches that focus on entire structure of the graphs. We propose an algorithm for the shared structure learning objective, evaluate its performance empirically, and compare with existing approaches on structure learning of single graphs.