Fast algorithms for finding nearest common ancestors
SIAM Journal on Computing
A new approach to the maximum flow problem
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Very simple methods for all pairs network flow analysis
SIAM Journal on Computing
A faster algorithm for finding the minimum cut in a graph
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
Journal of the ACM (JACM)
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Cut tree algorithms: an experimental study
Journal of Algorithms
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Graph Data
Partitioning of Web graphs by community topology
WWW '05 Proceedings of the 14th international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Tools for large graph mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Managing and Mining Graph Data
Managing and Mining Graph Data
Efficient and simple generation of random simple connected graphs with prescribed degree sequence
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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A fundamental and very useful operation in graphs is the computation of the proximity between nodes, i.e., the degree of dissimilarity (or similarity) between two nodes v and u. This is an important tool both in graph databases and graph mining applications, because it provides the base to support more complex tasks such as graph partitioning, clustering, classification, to name a few. All methods proposed in the literature assume that proximity is computed on a single graph by using a single distance measure. In addition, most of them focus on the proximity between node pairs. In this work, we present for the first time, scalable algorithms that: (i) they support proximity computation in multiple graph instances, (ii) they enable the utilization of several distance measures, (iii) they support proximity queries around a source node without limiting to node pairs and (iv) they support extensions for metric-based and skyline query processing. The main result of our work is the design of Threshold Algorithms for Graphs (denoted as TAGs), which are studied and evaluated experimentally by using real-life as well as synthetic graphs, based on both the G(n, p) Erdõs-Rényi model and power law degree distributions.