. The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph is a fascinating object of study: it has several hundred million nodes today, over a billion links, and appears to grow exponentially with time. There are many reasons --- mathematical, sociological, and commercial --- for studying the evolution of this graph. In this paper we begin by describing two algorithms that operate on the Web graph, addressing problems from Web search and automatic community discovery. We then report a number of measurements and properties of this graph that manifested themselves as we ran these algorithms on the Web. Finally, we observe that traditional random graph models do not explain these observations, and we propose a new family of random graph models. These models point to a rich new sub-field of the study of random graphs, and raise questions about the analysis of graph algorithms on the Web. 1 Overview Few events in the history of comput...

Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances. For example, collective inference could be used to simultaneously classify a set of hyperlinked documents or infer the legitimacy of a set of related financial transactions. Several recent studies indicate that collective inference can significantly reduce classification error when compared with traditional inference techniques. We investigate the underlying mechanisms for this error reduction by reviewing past work on collective inference and characterizing different types of statistical models used for making inference in relational data. We show important differences among these models, and we characterize the necessary and sufficient conditions for reduced classification error based on experiments with real and simulated data.

Abstract The World Wide Web contains an enormous amount of information, but it can be exceedingly difficult for users to locate resources that are both high in quality and relevant to their information needs. We develop algorithms that exploit the hyperlink structure of the WWW for information discovery and categorization, the construction of high-quality resource lists, and the analysis of on-line hyperlinked communities.

We develop the relational topic model (RTM), a model of documents and the links between them. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and learning algorithms based on variational methods and evaluate the predictive performance of the RTM for large networks of scientific abstracts and web documents. 1

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news. 1. Introduction. Network data

Web usage mining can be very useful to search engines. This paper proposes a novel effective approach to exploit the relationships among users, queries and resources based on the search engine's log. How this method can be applied is illustrated a Chinese image search engine.

Collective classification techniques jointly infer all class labels of a relational data set, using the inferences about one class label to influence inferences about related class labels. Typical collective classification schemes use computationally-intensive iterative algorithms or approximate joint inference techniques. Kou and Cohen recently introduced an efficient relational model based on stacking that, despite its simplicity, performs equivalently to more sophisticated joint inference approaches. This stacked relational model trains on the inferred labels of related instances, instead of the true labels which are not typically present at inference time. This permits the use of efficient exact inference in place of more computationally-intensive approximate joint inference. There are at least two possible causes for the unexpected high performance of the stacked approach: a reduction in inference bias (resulting from training on inferred rather than true labels) or a reduction in inference variance (due to the use of exact rather than approximate inference). Using experiments on both real and synthetic data, we show that the primary cause for the performance of the stacked model is the reduction in bias from learning the stacked model on inferred labels rather than the true labels. The reduction in variance due to conditional inference also contributes to the effect but it is not as strong. In addition, we show that the performance of the joint inference and stacked learners can be attributed to an implicit weighting of local and relational features at learning time. 1

Abstract. In this paper, we propose an automatic text classification method based on word sense disambiguation. We use “hood ” algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University. Keywords: disambiguation, word sense, text classification, WordNet.

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, i.e., discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news. 1. Introduction. Network data

Abstract—Labeled Data is scarce. Most statistical machine learning techniques rely on the availability of a large labeled corpus for building robust models for prediction and classification. In this paper we present a Higher Order Collective Classifier (HOCC) based on Higher Order Learning, a statistical machine learning technique that leverages latent information present in cooccurrences of items across records. These techniques violate the IID assumption that underlies most statistical machine learning techniques and have in prior work outperformed first order techniques in the presence of very limited data. We present results of applying HOCC to two different network data sets, first for detection and classification of anomalies in a Border Gateway Protocol dataset and second for building models of users from Network File System calls to perform masquerade detection. The precision of our system has been shown to be 30 % better than the standard Naive Bayes technique for masquerade detection. These results indicate that HOCC can successfully model a variety of network events and can be applied to solve difficult problems in security using the general framework proposed. I.