ar X iv : 1 20 1 . 03 75 v 1 [ cs . S I ] 1 J an 2 01 2 Gossip on Weighted Networks

ar X iv : 1 20 1 . 03 75 v 2 [ cs . S I ] 5 M ay 2 01 2 Gossip on Weighted Networks

We investigate how suitable a weighted network is for gossip spreading. The proposed model is based on the gossip spreading model introduced by Lind et.al. on unweighted networks. Weight represents “friendship. Potential spreader prefers not to spread if the victim of gossip is a “close friend”. Gossip spreading is related to the triangles and cascades of triangles. It gives more insight about ...

ar X iv : c s / 01 10 03 8 v 1 [ cs . C C ] 1 8 O ct 2 00 1 Counting Is Easy †

For any fixed k, a remarkably simple single-tape Turing machine can simulate k independent counters in real time.

ar X iv : 0 80 8 . 01 63 v 1 [ cs . D S ] 1 A ug 2 00 8 Twice - Ramanujan Sparsifiers ∗

We prove that for every d > 1 and every undirected, weighted graph G = (V, E), there exists a weighted graph H with at most ⌈d |V |⌉ edges such that for every x ∈ IR , 1 ≤ x T LHx x LGx ≤ d + 1 + 2 √ d d + 1 − 2 √ d , where LG and LH are the Laplacian matrices of G and H , respectively.

ar X iv : h ep - t h / 01 03 09 3 v 1 1 3 M ar 2 00 1 Physical Aspects of the Space - Time Torsion

Optimal Gossip Algorithms for Exact and Approximate Quantile Computations

This paper gives drastically faster gossip algorithms to compute exact and approximate quantiles. Gossip algorithms, which allow each node to contact a uniformly random other node in each round, have been intensely studied and been adopted in many applications due to their fast convergence and their robustness to failures. Kempe et al. [KDG03, FOCS’03] gave gossip algorithms to compute importan...