The problem of traversing probabilistic graphs has been studied for a long time. This is because most of the graphs that we come across, whether it is a network of roads or a set of network links are probabilistic in nature. A probabilistic graph is one where there is a probability associated

hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat

ments for the degree of Doctor of Science. A probabilistic graph is a linear graph in which both nodes and links are subject to random erasure. Such a graph may be thought of as an idealized model of a communi-cation network in which switching centers (nodes) and information channels (links) either

. The model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as Graph-Clear. This extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how

Abstract — Large probabilistic graphs arise in various domains spanning from social networks to biological and communication networks. An important query in these graphs is the k nearestneighbor query, which involves finding and reporting the k closest nodes to a specific node. This query assumes

This paper introduces probabilistic pattern queries over complex probabilistic graphs, a theoretical graph model proposed by us recently for dealing with complex probabilistic graph data of modern applications characterized by uncertainty and imprecision. Effective algorithms implementing

Abstract—We study the problem of clustering probabilistic graphs. Similar to the problem of clustering standard graphs, probabilistic graph clustering has numerous applications, such as finding complexes in probabilistic protein-protein interaction networks and discovering groups of users

We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large networks. We define a probabilistic similarity measure for nodes, and then apply clustering methods to find groups of nodes. Finally, a

of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related

Abstract. The probabilistic graphs framework models the uncertainty inherent in real-world domains by means of probabilistic edges whose value quantifies the likelihood of the edge existence or the strength of the link it represents. The goal of this paper is to provide a learning method to compute