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15
A Faster Algorithm for Betweenness Centrality
 Journal of Mathematical Sociology
, 2001
"... The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network. ..."
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The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network.
Identification of Influencers Measuring Influence in Customer Networks
"... Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through selfreplicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number ..."
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Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through selfreplicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. In case data about the customer network is available, centrality measures can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select customers for a marketing campaign, an issue that little prior research has addressed. In this paper, we present the results of computational experiments based on real network data to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process. More importantly, we found that in most cases the simple outdegree centrality outperforms almost all other measures. Only the SenderRank, a computationally much more complex measure that we introduce in this paper, achieved a comparable performance. Key words: customer relationship management, viral marketing, centrality, network theory 1.
A Flexible Architecture for PrivacyAware Trust Management
"... This paper is available online at www.jtaer.com DOI: 10.4067/S071818762010000200006 ..."
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This paper is available online at www.jtaer.com DOI: 10.4067/S071818762010000200006
Efficient Processing of Distance Queries in Large Graphs: A Vertex Cover Approach ABSTRACT
"... We propose a novel diskbased index for processing singlesource shortest path or distance queries. The index is useful in a wide range of important applications (e.g., network analysis, routing planning, etc.). Our index is a treestructured index constructed based on the concept of vertex cover. W ..."
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We propose a novel diskbased index for processing singlesource shortest path or distance queries. The index is useful in a wide range of important applications (e.g., network analysis, routing planning, etc.). Our index is a treestructured index constructed based on the concept of vertex cover. We propose an I/Oefficient algorithm to construct the index when the input graph is too large to fit in main memory. We give detailed analysis of I/O and CPU complexity for both index construction and query processing, and verify the efficiency of our index for query processing in massive realworld graphs.
Network Creation Games with Disconnected Equilibria
, 2008
"... In this paper we extend a popular noncooperative network creation game (NCG) [11] to allow for disconnected equilibrium networks. There are n players, each is a vertex in a graph, and a strategy is a subset of players to build edges to. For each edge a player must pay a cost α, and the individual ..."
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In this paper we extend a popular noncooperative network creation game (NCG) [11] to allow for disconnected equilibrium networks. There are n players, each is a vertex in a graph, and a strategy is a subset of players to build edges to. For each edge a player must pay a cost α, and the individual cost for a player represents a tradeoff between edge costs and shortest path lengths to all other players. We extend the model to a penalized game (PCG), for which we reduce the penalty for a pair of disconnected players to a finite value β. We prove that the PCG is not a potential game, but pure Nash equilibria always exist, and pure strong equilibria exist in many cases. We provide tight conditions under which disconnected (strong) Nash equilibria can evolve. Components of these equilibria must be (strong) Nash equilibria of a smaller NCG. But in contrast to the NCG, for the vast majority of parameter values no tree is a stable component. Finally, we show that the price of anarchy is Θ(n), several orders of magnitude larger than in the NCG. Perhaps surprisingly, the price of anarchy for strong equilibria increases only to at most 4.
Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks
"... Abstract: The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory ne ..."
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Abstract: The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.
Faster Evaluation of ShortestPath Based Centrality Indices
, 2000
"... Centrality indices are an important tool in network analysis, and many of them are derived from the set of all shortest paths of the underlying graph. The socalled betweenness centrality index is essential for the analysis of social networks, but most costly to compute. Currently, the fastest known ..."
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Centrality indices are an important tool in network analysis, and many of them are derived from the set of all shortest paths of the underlying graph. The socalled betweenness centrality index is essential for the analysis of social networks, but most costly to compute. Currently, the fastest known algorithms require Theta(n³) time and Theta(n²) space, where n is the number of vertices. Motivated by the fastgrowing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run in O(n(m + n)) or O(n(m + n log n)) time on unweighted or weighted graphs, respectively, where m is the number of edges. Since these algorithms simply augment singlesource shortestpaths computations, all standard centrality indices based on shortest paths can now be computed uniformly in one framework. Experimental evidence is provided that this substantially increases the range of network...
A Unifying Framework for Behaviorbased Trust Models
"... Abstract. Trust models have been touted to facilitate cooperation among unknown entities. Existing behaviorbased trust models typically include a fixed evaluation scheme to derive the trustworthiness of an entity from knowledge about its behavior in previous interactions. This paper in turn propose ..."
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Abstract. Trust models have been touted to facilitate cooperation among unknown entities. Existing behaviorbased trust models typically include a fixed evaluation scheme to derive the trustworthiness of an entity from knowledge about its behavior in previous interactions. This paper in turn proposes a framework for behaviorbased trust models for open environments with the following distinctive characteristic. Based on a relational representation of behaviorspecific knowledge, we propose a trustpolicy algebra allowing for the specification of a wide range of trustevaluation schemes. A key observation is that the evaluation of the standing of an entity in the network of peers requires centrality indices, and we propose a firstclass operator of our algebra for computation of centrality measures. This paper concludes with some preliminary performance experiments that confirm the viability of our approach. 1
Structural Comparison of Cognitive Associative Networks in Two Populations
"... The cognitive associative structure of two populations was studied using network analysis of freeword associations. Structural differences in the associative networks were compared using measures of network centralization, size, density and path length. These measures are closely aligned with cogni ..."
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The cognitive associative structure of two populations was studied using network analysis of freeword associations. Structural differences in the associative networks were compared using measures of network centralization, size, density and path length. These measures are closely aligned with cognitive theories describing the organization of knowledge and retrieval of concepts from memory. Size and centralization of semantic structures were larger for college students than for seventh graders, while density, clustering and average pathlength were similar. Findings presented reveal that subpopulations might have very different cognitive associative networks. This study suggests that graph theory and network analysis methods are useful in mapping differences in associative structures across groups.
ANALYSIS OF LAYERED SOCIAL NETWORKS
, 2006
"... contained in this dissertation are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ..."
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contained in this dissertation are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the