The field of graph sampling and network design is moving towards developing more efficient algorithms for sampling and analyzing large-scale networks. Recent research has focused on improving the scalability and accuracy of these algorithms, with a particular emphasis on planar graphs and random networks. Notably, new algorithms have been proposed for sampling tree-weighted partitions and approximating graph frequency vectors in sublinear time. These advances have the potential to impact a wide range of applications, from computational redistricting to distributed network design. Some noteworthy papers in this area include:

• A paper that presents a new algorithm for sampling from the balanced tree-weighted 2-partition distribution directly, without first sampling a spanning tree, achieving expected linear time O(n).
• A paper that proposes a robust topology obfuscation scheme, RoTO, which eliminates the need for perfect probe control or specific attacker models, and demonstrates improved topology obfuscation performance through optimization of an upper bound on attacker success probability.