The dynamics of self-evaluation
Applied Mathematics and Computation
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
IEEE Transactions on Knowledge and Data Engineering
WWW '03 Proceedings of the 12th international conference on World Wide Web
A learning algorithm for web page scoring systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Processing directed acyclic graphs with recursive neural networks
IEEE Transactions on Neural Networks
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This paper emphasizes some intriguing links between neural computation on graphical domains and social networks, like those used in nowadays search engines to score the page authority. It is pointed out that the introduction of web domains creates a unified mathematical framework for these computational schemes. It is shown that one of the major limitations of currently used connectionist models, namely their scarce ability to capture the topological features of patterns, can be effectively faced by computing the node rank according to social-based computation, like Google's PageRank. The main contribution of the paper is the introduction of a novel graph spectral notion, which can be naturally used for the graph isomorphism problem. In particular, a class of graphs is introduced for which the problem is proven to be polynomial. It is also pointed out that the derived spectral representations can be nicely combined with learning, thus opening the doors to many applications typically faced within the framework of neural computation.