Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Estimating node similarity from co-citation in a spatial graph model
Proceedings of the 2010 ACM Symposium on Applied Computing
Random dot product graph models for social networks
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
A Course on the Web Graph
Some typical properties of the spatial preferred attachment model
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
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This paper discusses the use of spatial graph models for the analysis of networks that do not have a direct spatial reality, such as web graphs, on-line social networks, or citation graphs. In a spatial graph model, nodes are embedded in a metric space, and link formation depends on the relative position of nodes in the space. It is argued that spatial models form a good basis for link mining: assuming a spatial model, the link information can be used to infer the spatial position of the nodes, and this information can then be used for clustering and recognition of node similarity. This paper gives a survey of spatial graph models, and discusses their suitability for link mining.