TY - GEN
T1 - Recovering the long-range links in augmented graphs
AU - Fraigniaud, Pierre
AU - Lebhar, Emmanuelle
AU - Lotker, Zvi
PY - 2008
Y1 - 2008
N2 - The augmented graph model, as introduced by Kleinberg (STOC 2000), is an appealing model for analyzing navigability in social networks. Informally, this model is defined by a pair (H,ℓ), where H is a graph in which inter-node distances are supposed to be easy to compute or at least easy to estimate. This graph is "augmented" by links, called long-range links, which are selected according to the probability distribution ℓ. The augmented graph model enables the analysis of greedy routing in augmented graphs G∈ ∈(H,ℓ). In greedy routing, each intermediate node handling a message for a target t selects among all its neighbors in G the one that is the closest to t in H and forwards the message to it. This paper addresses the problem of checking whether a given graph G is an augmented graph. It answers part of the questions raised by Kleinberg in his Problem 9 (Int. Congress of Math. 2006). More precisely, given G∈ ∈(H,ℓ), we aim at extracting the base graph H and the long-range links R out of G. We prove that if H has high clustering coefficient and H has bounded doubling dimension, then a simple local maximum likelihood algorithm enables to partition the edges of G into two sets H′ and R′ such that E(H) ∪ H′ and the edges in H′\E(H) are of small stretch, i.e., the map H is not perturbed too greatly by undetected long-range links remaining in H′. The perturbation is actually so small that we can prove that the expected performances of greedy routing in G using the distances in H′ are close to the expected performances of greedy routing using the distances in H. Although this latter result may appear intuitively straightforward, since H′\E(H), it is not, as we also show that routing with a map more precise than H may actually damage greedy routing significantly. Finally, we show that in absence of a hypothesis regarding the high clustering coefficient, any local maximum likelihood algorithm extracting the long-range links can miss the detection of at least Ω(n 5ε /logn) long-range links of stretch at least Ω(n 1/5∈-∈ε ) for any 0∈<∈ε <∈1/5, and thus the map H cannot be recovered with good accuracy.
AB - The augmented graph model, as introduced by Kleinberg (STOC 2000), is an appealing model for analyzing navigability in social networks. Informally, this model is defined by a pair (H,ℓ), where H is a graph in which inter-node distances are supposed to be easy to compute or at least easy to estimate. This graph is "augmented" by links, called long-range links, which are selected according to the probability distribution ℓ. The augmented graph model enables the analysis of greedy routing in augmented graphs G∈ ∈(H,ℓ). In greedy routing, each intermediate node handling a message for a target t selects among all its neighbors in G the one that is the closest to t in H and forwards the message to it. This paper addresses the problem of checking whether a given graph G is an augmented graph. It answers part of the questions raised by Kleinberg in his Problem 9 (Int. Congress of Math. 2006). More precisely, given G∈ ∈(H,ℓ), we aim at extracting the base graph H and the long-range links R out of G. We prove that if H has high clustering coefficient and H has bounded doubling dimension, then a simple local maximum likelihood algorithm enables to partition the edges of G into two sets H′ and R′ such that E(H) ∪ H′ and the edges in H′\E(H) are of small stretch, i.e., the map H is not perturbed too greatly by undetected long-range links remaining in H′. The perturbation is actually so small that we can prove that the expected performances of greedy routing in G using the distances in H′ are close to the expected performances of greedy routing using the distances in H. Although this latter result may appear intuitively straightforward, since H′\E(H), it is not, as we also show that routing with a map more precise than H may actually damage greedy routing significantly. Finally, we show that in absence of a hypothesis regarding the high clustering coefficient, any local maximum likelihood algorithm extracting the long-range links can miss the detection of at least Ω(n 5ε /logn) long-range links of stretch at least Ω(n 1/5∈-∈ε ) for any 0∈<∈ε <∈1/5, and thus the map H cannot be recovered with good accuracy.
UR - http://www.scopus.com/inward/record.url?scp=48249128722&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69355-0_10
DO - 10.1007/978-3-540-69355-0_10
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AN - SCOPUS:48249128722
SN - 3540693262
SN - 9783540693260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 118
BT - Structural Information and Communication Complexity - 15th International Colloquium, SIROCCO 2008, Proceedings
PB - Springer Verlag
T2 - 15th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2008
Y2 - 17 June 2008 through 20 June 2008
ER -