Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Focused Crawling Using Context Graphs
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Social networks, incentives, and search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using structure indices for efficient approximation of network properties
Proceedings of the 12th 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
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
Depth of field and cautious-greedy routing in social networks
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Fast approximate similarity search based on degree-reduced neighborhood graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing decentralized service discovery in open service-oriented multi-agent systems
Autonomous Agents and Multi-Agent Systems
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We propose a new algorithm for finding a target node in a network whose topology is known only locally. We formulate this task as a problem of decision making under uncertainty and use the statistical properties of the graph to guide this decision. This formulation uses the homophily and degree structure of the network simultaneously, differentiating our algorithm from those previously proposed in the literature. Because homophily and degree disparity are characteristics frequently observed in real-world networks, the algorithm we propose is applicable to a wide variety of networks, including two families that have received much recent attention: small-world and scale-free networks.