NeurIPS 2019

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Poster

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models

Aditya Gangrade · Praveen Venkatesh · Bobak Nazer · Venkatesh Saligrama

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #236

in Theory -- Information Theory »

We propose and analyze the problems of \textit{community goodness-of-fit and two-sample testing} for stochastic block models (SBM), where changes arise due to modification in community memberships of nodes. Motivated by practical applications, we consider the challenging sparse regime, where expected node degrees are constant, and the inter-community mean degree ($b$) scales proportionally to intra-community mean degree ($a$). Prior work has sharply characterized partial or full community recovery in terms of a ``signal-to-noise ratio'' ($\mathrm{SNR}$) based on $a$ and $b$. For both problems, we propose computationally-efficient tests that can succeed far beyond the regime where recovery of community membership is even possible. Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$. Conversely, in the small change regime, $s \ll \sqrt{n}$, via an information theoretic lower bound, we show that, surprisingly, no algorithm can do better than the na\"ive algorithm that first estimates the community up to $O(s)$ errors and then detects changes. We validate these phenomena numerically on SBMs and on real-world datasets as well as Markov Random Fields where we only observe node data rather than the existence of links.

Author Information

Aditya Gangrade (Boston University)

Praveen Venkatesh (Carnegie Mellon University)

Bobak Nazer (Boston University)

Venkatesh Saligrama (Boston University)

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