Search

Previous studies on network mining have focused primarily on learning a single task (such as classification or community detection) on a given network. This paper considers the problem of multi-task learning on heterogeneous network data. Specifically, we present a novel framework that enables one to perform classification on one network and community detection in another related network. Multi-task learning is accomplished by introducing a joint objective function that must be optimized to ensure the classes in one network are consistent with the link structure, nodal attributes, as well as the communities detected in another network. We provide both theoretical and empirical analysis of the framework. We also show that the framework can be extended to incorporate prior information about the correspondences between the clusters and classes in different networks. Experiments performed on both real-world and synthetic data sets demonstrate the effectiveness of the joint framework compared to applying classification and community detection algorithms on each network separately. [ABSTRACT FROM AUTHOR]

Incorporating semantic knowledge from an ontology into document clustering is an important but challenging problem. While numerous methods have been developed, the value of using such an ontology is still not clear. We show in this paper that an ontology can be used to greatly reduce the number of features needed to do document clustering. Our hypothesis is that polysemous and synonymous nouns are both relatively prevalent and fundamentally important for document cluster formation. We show that nouns can be efficiently identified in documents and that this alone provides improved clustering. We next show the importance of the polysemous and synonymous nouns in clustering and develop a unique approach that allows us to measure the information gain in disambiguating these nouns in an unsupervised learning setting. In so doing, we can identify a core subset of semantic features that represent a text corpus. Empirical results show that by using core semantic features for clustering, one can reduce the number of features by 90% or more and still produce clusters that capture the main themes in a text corpus. [ABSTRACT FROM AUTHOR]

conceptual framework for climate change assessments of international market systems that involve long-term investments is proposed. The framework is a hybrid of dynamic and static modeling. Dynamic modeling is used for those system components for which temporally continuous modeling is possible, while fixed time slices are used for other system components where it can be assumed that underlying assumptions are held constant within the time slices but allowed to vary between slices. An important component of the framework is the assessment of the 'metauncertainty' arising from the structural uncertainties of a linked sequence of climate, production, trade and decision-making models. The impetus for proposing the framework is the paucity of industry-wide assessments for market systems with multiple production regions and long-term capital investments that are vulnerable to climate variations and change, especially climate extremes. The proposed framework is pragmatic, eschewing the ideal for the tractable. Even so, numerous implementation challenges are expected, which are illustrated using an example industry. The conceptual framework is offered as a starting point for further discussions of strategies and approaches for climate change impact, vulnerability and adaptation assessments for international market systems. [ABSTRACT FROM AUTHOR]

Web robots are software programs that automatically traverse the hyperlink structure of the World Wide Web in order to locate and retrieve information. There are many reasons why it is important to identify visits by Web robots and distinguish them from other users. First of all, e-commerce retailers are particularly concerned about the unauthorized deployment of robots for gathering business intelligence at their Web sites. In addition, Web robots tend to consume considerable network bandwidth at the expense of other users. Sessions due to Web robots also make it more difficult to perform clickstream analysis effectively on the Web data. Conventional techniques for detecting Web robots are often based on identifying the IP address and user agent of the Web clients. While these techniques are applicable to many well-known robots, they may not be sufficient to detect camouflaged and previously unknown robots. In this paper, we propose an alternative approach that uses the navigational patterns in the click-stream data to determine if it is due to a robot. Experimental results on our Computer Science department Web server logs show that highly accurate classification models can be built using this approach. We also show that these models are able to discover many camouflaged and previously unidentified robots. [ABSTRACT FROM AUTHOR]