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282
Random walks for image segmentation
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2006
"... A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the ..."
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Cited by 387 (21 self)
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A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a highquality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs.
Social Network Analysis for Routing in Disconnected Delaytolerant MANETs
, 2007
"... Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low endtoend delay in a disconnected network graph where nodes may move fr ..."
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Cited by 276 (1 self)
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Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low endtoend delay in a disconnected network graph where nodes may move freely. This paper presents a multidisciplinary solution based on the consideration of the socalled small world dynamics which have been proposed for economy and social studies and have recently revealed to be a successful approach to be exploited for characterising information propagation in wireless networks. To this purpose, some bridge nodes are identified based on their centrality characteristics, i.e., on their capability to broker information exchange among otherwise disconnected nodes. Due to the complexity of the centrality metrics in populated networks the concept of ego networks is exploited where nodes are not required to exchange information about the entire network topology, but only locally available information is considered. Then SimBet Routing is proposed which exploits the exchange of preestimated ‘betweenness’ centrality metrics and locally determined social ‘similarity’ to the destination node. We present simulations using real trace data to demonstrate that SimBet Routing results in delivery performance close to Epidemic Routing but with significantly reduced overhead. Additionally, we show that SimBet Routing outperforms PRoPHET Routing, particularly when the sending and receiving nodes have low connectivity.
Randomwalk computation of similarities between nodes of a graph, with application to collaborative recommendation
 IEEE Transactions on Knowledge and Data Engineering
"... ABSTRACT This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markovchain model of random walk through the database. More precisely, we compute quantities (the average commu ..."
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Cited by 194 (19 self)
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ABSTRACT This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markovchain model of random walk through the database. More precisely, we compute quantities (the average commute time, the pseudoinverse of the Laplacian matrix of the graph, etc) that provide similarities between any pair of nodes, having the nice property of increasing when the number of paths connecting those elements increases and when the "length" of paths decreases. It turns out that the square root of the average commute time is a Euclidean distance and that the pseudoinverse of the Laplacian matrix is a kernel (it contains innerproducts closely related to commute times). A procedure for computing the subspace projection of the node vectors of the graph that preserves as much variance as possible in terms of the commutetime distance a principal components analysis (PCA) of the graph is also introduced. This graph PCA provides a nice interpretation to the "Fiedler vector", widely used for graph partitioning. The model is evaluated on a collaborativerecommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. Experimental results on the MovieLens database show that the Laplacianbased similarities perform well in comparison with other methods. The model, which nicely fits into the socalled "statistical relational learning" framework, could also be used to compute document or word similarities, and, more generally, could be applied to machinelearning and patternrecognition tasks involving a database. * François Fouss, Alain Pirotte and Marco Saerens are with the
The slashdot zoo: Mining a social network with negative edges
 In WWW
, 2009
"... christian.bauckhage ..."
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Social Network Analysis for Information Flow in Disconnected DelayTolerant MANETs
"... Abstract—Message delivery in sparse mobile ad hoc networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low endtoend delay in a disconnected network graph where nodes ma ..."
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Cited by 58 (0 self)
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Abstract—Message delivery in sparse mobile ad hoc networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low endtoend delay in a disconnected network graph where nodes may move freely. We cast this challenge as an information flow problem in a social network. This paper presents social network analysis metrics that may be used to support a novel and practical forwarding solution to provide efficient message delivery in disconnected delaytolerant MANETs. These metrics are based on social analysis of a node’s past interactions and consists of three locally evaluated components: a node’s “betweenness ” centrality (calculated using ego networks), a node’s social “similarity ” to the destination node, and a node’s tie strength relationship with the destination node. We present simulations using three real trace data sets to demonstrate that by combining these metrics delivery performance may be achieved close to Epidemic Routing but with significantly reduced overhead. Additionally, we show improved performance when compared to PRoPHET Routing. Index Terms—Delay and disruptiontolerant networks, MANETs, sparse networks, ego networks, social network analysis.
Who's Who in Networks. Wanted: The Key Player
, 2005
"... Finite population noncooperative games with linearquadratic utilities, where each player decides how much action she exerts, can be interpreted as a network game with local payoff complementarities, together with a globally uniform payoff substitutability component and an ownconcavity effect. For ..."
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Cited by 58 (3 self)
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Finite population noncooperative games with linearquadratic utilities, where each player decides how much action she exerts, can be interpreted as a network game with local payoff complementarities, together with a globally uniform payoff substitutability component and an ownconcavity effect. For these games, the Nash equilibrium action of each player is proportional to her Bonacich centrality in the network of local complementarities, thus establishing a bridge with the sociology literature on social networks. We then analyze a policy that consists of targeting the key player, that is, the player who, once removed, leads to the optimal change in aggregate activity. We provide a geometric characterization of the key player identified with an intercentrality measure, which takes into account both a player’s centrality and her contribution to the centrality of the others.
Centrality estimation in large networks
 INTL. JOURNAL OF BIFURCATION AND CHAOS, SPECIAL ISSUE ON COMPLEX NETWORKS’ STRUCTURE AND DYNAMICS
, 2007
"... Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (SSSP) problem from every node. Therefore, exact computation is ..."
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Cited by 55 (0 self)
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Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.
Fast Approximation of Matrix Coherence and Statistical Leverage
"... The statistical leverage scores of a matrix A are the squared rownorms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recentlypopular problems such as matrix completion and Nyströmbased lowrank matrix ..."
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Cited by 53 (11 self)
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The statistical leverage scores of a matrix A are the squared rownorms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recentlypopular problems such as matrix completion and Nyströmbased lowrank matrix approximation as well as in largescale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary n×d matrix A, with n ≫ d, and that returns as output relativeerror approximations to all n of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of n and d) in O(nd logn) time, as opposed to the O(nd 2) time required by the naïve algorithm that involves computing an orthogonal basis for the range of A. Our analysis may be viewed in terms of computing a relativeerror approximation to an underconstrained leastsquares approximation problem, or, relatedly, it may be viewed as an application of JohnsonLindenstrauss type ideas. Several practicallyimportant extensions of our basic result are also described, including the approximation of socalled crossleverage scores, the extension of these ideas to matrices with n≈d, and the extension to streaming environments.
A regularization framework for learning from graph data
 ICML Workshop on Statistical Relational Learning
, 2004
"... The data in many realworld problems can be thought of as a graph, such as the web, coauthor networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction problems. We also show that the method can ..."
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Cited by 51 (1 self)
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The data in many realworld problems can be thought of as a graph, such as the web, coauthor networks, and biological networks. We propose a general regularization framework on graphs, which is applicable to the classification, ranking, and link prediction problems. We also show that the method can be explained as lazy random walks. We evaluate the method on a number of experiments. 1.