Elements of information theory
Elements of information theory
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
ACM Transactions on Internet Technology (TOIT)
PageRank as a function of the damping factor
WWW '05 Proceedings of the 14th international conference on World Wide Web
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Finding related pages using Green measures: an illustration with Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Dynamics, Robustness and Fragility of Trust
Formal Aspects in Security and Trust
Quantifying and qualifying trust: spectral decomposition of trust networks
FAST'10 Proceedings of the 7th International conference on Formal aspects of security and trust
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
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We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneously evolving computational systems is that the data are not uniformly structured. An interesting approach is to try to extract the semantical content of the data from their distribution among the nodes. A concept is then identified by finding the community of nodes that share it. The task of data structuring is thus reduced to the task of finding the network communities, as groups of nodes that together perform some non-local data processing. Towards this goal, we extend the ranking methods from nodes to paths. This allows us to extract some information about the likely flow biases from the available static information about the network.