On power-law relationships of the Internet topology
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The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
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Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Random Evolution in Massive Graphs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
On Random Intersection Graphs: The Subgraph Problem
Combinatorics, Probability and Computing
Graphs over time: densification laws, shrinking diameters and possible explanations
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Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Statistical properties of community structure in large social and information networks
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Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social search in "Small-World" experiments
Proceedings of the 18th international conference on World wide web
Proceedings of the forty-first annual ACM symposium on Theory of computing
Concentration of Measure for the Analysis of Randomized Algorithms
Concentration of Measure for the Analysis of Randomized Algorithms
The effect of power-law degrees on the navigability of small worlds: [extended abstract]
Proceedings of the 28th ACM symposium on Principles of distributed computing
On the searchability of small-world networks with arbitrary underlying structure
Proceedings of the forty-second ACM symposium on Theory of computing
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Navigational efficiency of broad vs. narrow folksonomies
Proceedings of the 23rd ACM conference on Hypertext and social media
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We demonstrate how a recent model of social networks ("Affiliation Networks", [21]) offers powerful cues in local routing within social networks, a theme made famous by sociologist Milgram's "six degrees of separation" experiments. This model posits the existence of an "interest space" that underlies a social network; we prove that in networks produced by this model, not only do short paths exist among all pairs of nodes but natural local routing algorithms can discover them effectively. Specifically, we show that local routing can discover paths of length O(log2 n) to targets chosen uniformly at random, and paths of length O(1) to targets chosen with probability proportional to their degrees. Experiments on the co-authorship graph derived from DBLP data confirm our theoretical results, and shed light into the power of one step of lookahead in routing algorithms for social networks.