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Better Approximation of Betweenness Centrality
"... Estimating the importance or centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications, ..."
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Estimating the importance or centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications, e.g., social network analysis or route planning. Since exact computation is prohibitive for large networks, approximation algorithms are important. In this paper, we propose a framework for unbiased approximation of betweenness that generalizes a previous approach by Brandes. Our best new schemes yield significantly better approximation than before for many real world inputs. In particular, we also get good approximations for the betweenness of unimportant nodes.
Efficient search ranking in social networks
 in CIKM, 2007
"... In social networks such as Orkut, www.orkut.com, a large portion of the user queries refer to names of other people. Indeed, more than 50 % of the queries in Orkut are about names of other users, with an average of 1.8 terms per query. Further, the users usually search for people with whom they main ..."
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In social networks such as Orkut, www.orkut.com, a large portion of the user queries refer to names of other people. Indeed, more than 50 % of the queries in Orkut are about names of other users, with an average of 1.8 terms per query. Further, the users usually search for people with whom they maintain relationships in the network. These relationships can be modelled as edges in a friendship graph, a graph in which the nodes represent the users. In this context, search ranking can be modelled as a function that depends on the distances among users in the graph, more specifically, of shortest paths in the friendship graph. However, application of this idea to ranking is not straightforward because the large size of modern social networks (dozens of millions of users) prevents efficient computation of shortest paths at query time. We overcome this by designing a ranking formula that strikes a balance between producing good results and reducing query processing time. Using data from the Orkut social network, which includes over 40 million users, we show that our ranking, augmented by this new signal, produces high quality results, while maintaining query processing time small.
Graph clustering with network structure indices
, 2007
"... Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the kmedoids algorithm and the GirvanNewman method based on edge betweenness centrality. We show that they can be effective at discovering the latent groups ..."
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Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the kmedoids algorithm and the GirvanNewman method based on edge betweenness centrality. We show that they can be effective at discovering the latent groups or communities that are defined by the link structure of a graph. However, both approaches rely on prohibitively expensive computations, given the size of modern relational data sets. Network structure indices (NSIs) are a proven technique for indexing network structure and efficiently finding short paths. We show how incorporating NSIs into these graph clustering algorithms can overcome these complexity limitations. We also present promising quantitative and qualitative evaluations of the modified algorithms on synthetic and real data sets. 1.
Discovering Correlated SpatioTemporal Changes in Evolving Graphs
 UNDER CONSIDERATION FOR PUBLICATION IN KNOWLEDGE AND INFORMATION SYSTEMS
, 2007
"... Graphs provide powerful abstractions of relational data, and are widely used in fields such as network management, web page analysis and sociology. While many graph representations of data describe dynamic and time evolving relationships, most graph mining work treats graphs as static entities. Our ..."
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Graphs provide powerful abstractions of relational data, and are widely used in fields such as network management, web page analysis and sociology. While many graph representations of data describe dynamic and time evolving relationships, most graph mining work treats graphs as static entities. Our focus in this paper is to discover regions of a graph that are evolving in a similar manner. To discover regions of correlated spatiotemporal change in graphs, we propose an algorithm called cSTAG. Whereas most clustering techniques are designed to find clusters that optimise a single distance measure, cSTAG addresses the problem of finding clusters that optimise both temporal and spatial distance measures simultaneously. We show the effectiveness of cSTAG using a quantitative analysis of accuracy on synthetic data sets, as well as demonstrating its utility on two large, reallife data sets, where one is the routing topology of the Internet, and the other is the dynamic graph of files accessed together on the 1998 World Cup official website.
Fax: +810117067832Fast Approximation Algorithm for the 1Median Problem
, 2012
"... We present a fast approximation algorithm for the 1median problem. Our algorithm can be applied to metric undirected graphs with node weight. Given a node v, our algorithm repeatedly executes a process of finding a node with higher centrality,in which an approximate centrality of each node v ’ is c ..."
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We present a fast approximation algorithm for the 1median problem. Our algorithm can be applied to metric undirected graphs with node weight. Given a node v, our algorithm repeatedly executes a process of finding a node with higher centrality,in which an approximate centrality of each node v ’ is calculated for the subgraph called the best kNSPGS of (v,v’). The best kNSPGS of (v,v’) is a subgraph that contains the shortest path tree of v, and approximate centralities of all the nodes v ’ for the subtrees can be calculated more efficiently than their exact centralities for the original graph. We empirically show that our algorithm runs much faster and has better approximation ratio than a sophisticated existing method called DTZ. We demonstrate the effectiveness of our algorithm through experiments. We can use graphs to describe many kinds of relationships in daily life. For example, consider a graph with node weight to describe the transportation network. The node weight means the customer’s demand and the edges mean transport routes with
Exploiting
"... network structure for active inference in collective classification ..."
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ολοκλήρου ή τμήματος αυτής, για εμπορικό σκοπό. Επιτρέπεται η ανατύπωση, αποθήκευση