From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Nonlinear dimensionality reduction by locally linear embedding.

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

Social networks are ubiquitous The discovery of close-knit clusters in these networks is of fundamental and practical interest Existing clustering criteria are limited in that clusters typically do not overlap, all vertices are clustered and/or external sparsity is ignored We introduce a new criterion that overcomes these limitations by combining internal density with external sparsity in a natural way This paper explores combinatorial properties of internally dense and externally sparse clusters A simple algorithm is given for provably finding such clusters assuming a sufficiently large gap between internal density and e sparsity Experiments show that the algorithm is able to identify over 90% of the clusters in real graphs, assuming conditions on external sparsity

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Finding Strongly Knit Clusters in Social Networks

We present a randomized algorithm that triangulates a simple polygon onn vertices inO(n log*n) expected time. The averaging in the analysis of running time is over the possible choices made by the algorithm; the bound holds for any input polygon.

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A fast las vegas algorithm for triangulating a simple polygon

We study the problem of orienting all the undirected edges of a mixed multigraph so as to preserve reachability. Extending work by Robbins and by Boesch and Tindell, we develop a linear-time algorithm to test whether there is an orientation that preserves strong connectivity and to construct such an orientation whenever possible. This algorithm makes no attempt to minimize distances in the resulting directed graph, and indeed the maximum distance, for example, can blow up by a factor proportional to the number of vertices in the graph. Extending work by Chvatal and Thomassen, we then prove that, if a mixed multigraph of radius r has any strongly connected orientation, it must have an orientation of radius at most 42 + Ar. The proof gives a polynomial-time algorithm for constructing such an orientation.

Strongly connected orientations of mixed multigraphs

The dynamic trees problem is that of maintaining a forest that changes over time through edge insertions and deletions. We can associate data with vertices or edges, and manipulate this data individually or in bulk, with operations that deal with whole paths or trees. Efficient solutions to this problem have numerous applications, particularly in algorithms for network flows and dynamic graphs in general. Several data structures capable of logarithmic-time dynamic tree operations have been proposed. The first was Sleator and Tarjan's ST-tree [16, 17], which represents a partition of the tree into paths. Although reasonably fast in practice, adapting ST-trees to different applications is nontrivial. Topology trees [9], top trees [3], and RC-trees [1] are based on tree contractions: they progressively combine vertices or edges to obtain a hierarchical representation of the tree. This approach is more flexible in theory, but all known implementations assume the trees have bounded degree; arbitrary trees are supported only after ternarization. We show how these two approaches can be combined (with very little overhead) to produce a data structure that is as generic as any other, very easy to adapt, and as practical as ST-trees.

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Self-adjusting top trees

This paper presents an analysis of the following load balancing algorithm. At each step, each node in a network examines the number of tokens at each of its neighbors and sends a token to each neighbor with at least 2d+1 fewer tokens, where d is the maximum degree of any node in the network. We show that within $O(\Delta / \alpha)$ steps, the algorithm reduces the maximum difference in tokens between any two nodes to at most $O((d^2 \log n)/\alpha)$, where $\Delta$ is the global imbalance in tokens (i.e., the maximum difference between the number of tokens at any node initially and the average number of tokens), n is the number of nodes in the network, and $\alpha$ is the edge expansion of the network. The time bound is tight in the sense that for any graph with edge expansion $\alpha$, and for any value $\Delta$, there exists an initial distribution of tokens with imbalance $\Delta$ for which the time to reduce the imbalance to even $\Delta/2$ is at least $\Omega(\Delta/\alpha)$. The bound on the final imbalance is tight in the sense that there exists a class of networks that can be locally balanced everywhere (i.e., the maximum difference in tokens between any two neighbors is at most 2d), while the global imbalance remains $\Omega((d^2 \log n) / \alpha)$. Furthermore, we show that upon reaching a state with a global imbalance of $O((d^2 \log n)/\alpha)$, the time for this algorithm to locally balance the network can be as large as $\Omega(n^{1/2})$. We extend our analysis to a variant of this algorithm for dynamic and asynchronous networks. We also present tight bounds for a randomized algorithm in which each node sends at most one token in each step.

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Robert E. Tarjan

Papers

Finding Strongly Knit Clusters in Social Networks

A fast las vegas algorithm for triangulating a simple polygon

Strongly connected orientations of mixed multigraphs

Self-adjusting top trees

Tight Analyses of Two Local Load Balancing Algorithms