Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Cavity approach to the first eigenvalue problem in a family of symmetric random sparse matrices

A methodology to analyze the properties of the first (largest) eigenvalue and its eigenvector is developed for large symmetric random sparse matrices utilizing the cavity method of statistical mechanics. Under a tree approximation, which is plausible for infinitely large systems, in conjunction with the introduction of a Lagrange multiplier for constraining the length of the eigenvector, the eigenvalue problem is reduced to a bunch of optimization problems of a quadratic function of a single variable, and the coefficients of the first and the second order terms of the functions act as cavity fields that are handled in cavity analysis. We show that the first eigenvalue is determined in such a way that the distribution of the cavity fields has a finite value for the second order moment with respect to the cavity fields of the first order coefficient. The validity and utility of the developed methodology are examined by applying it to two analytically solvable and one simple but non-trivial examples in conjunction with numerical justification.Comment: 11 pages, 4 figures, to be presented at IW-SMI2010, Kyoto, March 7-10, 201

Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change

Copyright in each article is held by the authors. Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action and Change (NRAC) has an active and loyal community. Since its inception in 1995, the workshop has been held seven times in conjunction with IJCAI, and has experienced growing success. We hope to build on this success again this eighth year with an interesting and fruitful day of discussion. The areas of reasoning about action, non-monotonic reasoning and belief revision are among the most active research areas in Knowledge Representation, with rich inter-connections and practical applications including robotics, agentsystems, commonsense reasoning and the semantic web. This workshop provides a unique opportunity for researchers from all three fields to be brought together at a single forum with the prime objectives of communicating important recent advances in each field and the exchange of ideas. As these fundamental areas mature it is vital that researchers maintain a dialog through which they can cooperatively explore common links. The goal of this workshop is to work against the natural tendency of such rapidly advancing fields to drift apart into isolated islands of specialization. This year, we have accepted ten papers authored by a diverse international community. Each paper has been subject to careful peer review on the basis of innovation, significance and relevance to NRAC. The high quality selection of work could not have been achieved without the invaluable help of the international Program Committee. A highlight of the workshop will be our invited speaker Professor Hector Geffner from ICREA and UPF in Barcelona, Spain, discussing representation and inference in modern planning. Hector Geffner is a world leader in planning, reasoning, and knowledge representation; in addition to his many important publications, he is a Fellow of the AAAI, an associate editor of the Journal of Artificial Intelligence Research and won an ACM Distinguished Dissertation Award in 1990

Spartan Daily, May 1, 1991

Volume 96, Issue 59https://scholarworks.sjsu.edu/spartandaily/8127/thumbnail.jp

Requiem banana man: banana farming in the commonwealth of Dominica

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