From your Block to our Block: How to Find Shared Structure between Stochastic Block Models over Multiple Graphs

TL;DR

This paper introduces the shared stochastic block modeling (SSBM) problem for multiple unaligned graphs, proposing algorithms to identify shared community structures, and demonstrates their effectiveness through extensive experiments.

Contribution

It formulates the SSBM problem, proves its NP-hardness, and develops practical algorithms for fitting shared SBMs across multiple graphs with different sizes and unaligned vertices.

Findings

Algorithms effectively identify shared blocks in synthetic data.

Methods perform well on real-world graph datasets.

Proposed approaches scale to large numbers of blocks.

Abstract

Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out if these graphs share blocks with similar connectivity structures? In this paper, we propose the shared stochastic block modeling (SSBM) problem, in which we model $n$ graphs using SBMs that share parameters of $s$ blocks. We show that fitting an SSBM is NP-hard, and consider two approaches to fit good models in practice. In the first, we directly maximize the likelihood of the shared model using a Markov chain Monte Carlo algorithm. In the second, we first fit an SBM for each graph and then select which blocks to share. We propose an integer linear program to find the optimal shared blocks and to scale to large numbers of blocks, we propose a fast greedy algorithm. Through extensive empirical evaluation on synthetic and real-world data, we show that our methods work well in practice.