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ACM SIGCOMM Computer Communication Review
Routing Algorithms for DHTs: Some Open Questions
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Peer-to-peer research at Stanford
ACM SIGMOD Record
Ulysses: A Robust, Low-Diameter, Low-Latency Peer-ti-Peer Network
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Computing the shortest path: A search meets graph theory
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
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The goal of this work is to identify the diameter, the maximum distance between any two nodes, of graphs that evolve over time. This problem is useful for many applications such as improving the quality of P2P networks. Our solution, G-Scale, can track the diameter of time-evolving graphs in the most efficient and correct manner. G-Scale is based on two ideas: (1) It estimates the maximal distances at any time to filter unlikely nodes that cannot be associated with the diameter, and (2) It maintains answer node pairs by exploiting the distances from a newly added node to other nodes. Our theoretical analyses show that G-Scale guarantees exactness in identifying the diameter. We perform several experiments on real and large datasets. The results show that G-Scale can detect the diameter significantly faster than existing approaches.