(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs

Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng

Neural Information Processing Systems 

We give the first efficient algorithms proven to succeed in the correlated Erdös-Rényi model (Pedarsani and Grossglauser, 2011). Specifically, we give a polynomial time algorithm for the graph similarity/hypothesis testing task which works for every constant level of correlation between the two graphs that can be arbitrarily close to zero.