The Marginal Benefit of Monitor Placement on Networks

Seeing Red: Locating People of Interest in Networks

“…There are two types of features: (a) Network structure-based features (1, 2, 3), and (b) Neighbor answer-based features (4,5,6,7,8). Inferred probability of being red…”

“…There are a multitude of sampling techniques for network exploration, including random walks ( [3], [11], [13]), biased random walks ( [9]), or walks combined with reversible Markov Chains( [2]), Bayesian methods( [8]), or standard exhaustive search algorithms like depth-first or breadth-first searches, such as [1,5,6,7,12]. However, these methods generally do not use node attributes.…”

Optimizing Network Discovery with Clever Walks

“…We say that a monitor on node i detects an edge ij if i and ij are incident, and i detects the label ij of the edge (i.e. the monitor discovers the label of the other end node of ij) [7]. This then implies that a monitor on node i detects a node j if there is an edge ij connecting them.…”

“…The work of Davis et al [7], presents a history of inferences and introduces a new one. Our research contrasts their algorithm with new variants in order to explore the choices one has in inferring a network.…”

“…Our research contrasts their algorithm with new variants in order to explore the choices one has in inferring a network. A comparison of performance of our algorithms against upper and lower bounds, as well as the established algorithm of [7] on each of these different test networks will be presented.…”