The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Mining the Web's Link Structure
Computer
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Web mining in soft computing framework: relevance, state of the art and future directions
IEEE Transactions on Neural Networks
Modeling self-developing biological neural networks
Neurocomputing
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Information Sciences: an International Journal
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In this paper, we propose a new network growth model and its learning algorithm to more precisely model such a real-world growing network as the Web. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We show that the proposed model exhibits a degree distribution with a power-law tail, which is an important characteristic of many large-scale real-world networks including the Web. Using real Web data, we experimentally show that predictive ability can be improved by incorporating directional attachment and community structure. Also, using synthetic data, we experimentally show that predictive ability can definitely be improved by incorporating community structure.