421 citations · 864 across the 15 of their papers we have counts for
7 papers · 1 filter
ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics
Omer Nebil Yaveroglu, Sean M. Fitzhugh, Maciej Kurant +3
Exponential-family random graph models (ERGMs) are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) propertie…
Graph Size Estimation
Maciej Kurant, Carter T. Butts, Athina Markopoulou
Many online networks are not fully known and are often studied via sampling. Random Walk (RW) based techniques are the current state-of-the-art for estimating nodal attributes and…
2.5K-Graphs: from Sampling to Generation
Minas Gjoka, Maciej Kurant, Athina Markopoulou
Understanding network structure and having access to realistic graphs plays a central role in computer and social networks research. In this paper, we propose a complete, and pract…
Coarse-Grained Topology Estimation via Graph Sampling
Maciej Kurant, Minas Gjoka, Yan Wang +3
Many online networks are measured and studied via sampling techniques, which typically collect a relatively small fraction of nodes and their associated edges. Past work in this ar…
Towards Unbiased BFS Sampling
Maciej Kurant, Athina Markopoulou, Patrick Thiran
Breadth First Search (BFS) is a widely used approach for sampling large unknown Internet topologies. Its main advantage over random walks and other exploration techniques is that a…
Walking on a Graph with a Magnifying Glass: Stratified Sampling via Weighted Random Walks
M. Kurant, M. Gjoka, C. T. Butts +1
Our objective is to sample the node set of a large unknown graph via crawling, to accurately estimate a given metric of interest. We design a random walk on an appropriately define…