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Drawing graphs to convey proximity: an incremental arrangement method
ACM Transactions on Computer-Human Interaction (TOCHI)
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
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The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational clustering for multi-type entity resolution
MRDM '05 Proceedings of the 4th international workshop on Multi-relational 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
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
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Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Centrality measures based on current flow
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing Network Structure with Shortest-Path Trees
ACM Transactions on Knowledge Discovery from Data (TKDD)
Using proximity to predict activity in social networks
Proceedings of the 21st international conference companion on World Wide Web
Clustered embedding of massive social networks
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A survey on proximity measures for social networks
Search Computing
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Network flows and the link prediction problem
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and extracting proximity in networks called “cycle-free effective conductance” (CFEC). Importantly, the measured proximity is accompanied with a proximity subgraph which allows assessing and understanding measured values. Our proximity calculation can handle more than two endpoints, directed edges, is statistically well behaved, and produces an effectiveness score for the computed subgraphs. We provide an efficient algorithm to measure and extract proximity. Also, we report experimental results and show examples for four large network datasets: a telecommunications calling graph, the IMDB actors graph, an academic coauthorship network, and a movie recommendation system.