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13
The slashdot zoo: Mining a social network with negative edges
 In WWW
, 2009
"... christian.bauckhage ..."
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Finding the bias and prestige of nodes in networks based on trust scores
 In WWW
, 2011
"... Many reallife graphs such as social networks and peertopeer networks capture the relationships among the nodes by using trust scores to label the edges. Important usage of such networks includes trust prediction, finding the most reliable or trusted node in a local subgraph, etc. For many of these ..."
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Many reallife graphs such as social networks and peertopeer networks capture the relationships among the nodes by using trust scores to label the edges. Important usage of such networks includes trust prediction, finding the most reliable or trusted node in a local subgraph, etc. For many of these applications, it is crucial to assess the prestige and bias of a node. The bias of a node denotes its propensity to trust/mistrust its neighbours and is closely related to truthfulness. If a node trusts all its neighbours, its recommendation of another node as trustworthy is less reliable. It is based on the idea that the recommendation of a highly biased node should weigh less. In this paper, we propose an algorithm to compute the bias and prestige of nodes in networks where the edge weight denotes the trust score. Unlike most other graphbased algorithms, our method works even when the edge weights are not necessarily positive. The algorithm is iterative and runs in O(km) timewherek is the number of iterations and m is the total number of edges in the network. The algorithm exhibits several other desirable properties. It converges to a unique value very quickly. Also, the error in bias and prestige values at any particular iteration is bounded. Further, experiments show that our model conforms well to social theories such as the balance theory (enemy of a friend is an enemy, etc.).
Analyses for Service Interaction Networks with applications to Service Delivery
"... One of the distinguishing features of the services industry is the high emphasis on people interacting with other people and serving customers rather than transforming physical goods like in the traditional manufacturing processes. It is evident that analysis of such interactions is an essential asp ..."
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One of the distinguishing features of the services industry is the high emphasis on people interacting with other people and serving customers rather than transforming physical goods like in the traditional manufacturing processes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. In this work we focus on learning individual and team behavior of different people or agents of a service organization by studying the patterns and outcomes of historical interactions. For each past interaction, we assume that only the list of participants and an outcome indicating the overall effectiveness of the interaction are known. Note that this offers limited information on the mutual (pairwise) compatibility of different participants. We develop the notion of service interaction networks which is an abstraction of the historical data and allows one to cast practical problems in a formal setting. We identify the unique characteristics of analyzing service interaction networks when compared to traditional analyses considered in social network analysis and establish a need for new modeling and algorithmic techniques for such networks. On the algorithmic front, we develop new algorithms to infer attributes of agents individually and in team settings. Our first algorithm is based on a novel modification to the eigenvector based centrality for ranking the agents and the second algorithm is an iterative update technique that can be applied for subsets of agents as well. One of the challenges of conducting research in this setting is the sensitive and proprietary nature of the data. Therefore, there is a need for a realistic simulator for studying service interaction networks. We present the initial version of our simulator that is geared to capture several characteristics of service interaction networks that arise in reallife.
A novel approach to propagating distrust
"... Trust propagation is a fundamental topic of study in the theory and practice of ranking and recommendation systems on networks. The Page Rank [9] algorithm ranks web pages by propagating trust throughout a network, and similar algorithms have been designed for recommendation systems. How might one a ..."
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Trust propagation is a fundamental topic of study in the theory and practice of ranking and recommendation systems on networks. The Page Rank [9] algorithm ranks web pages by propagating trust throughout a network, and similar algorithms have been designed for recommendation systems. How might one analogously propagate distrust as well? This is a question of practical importance and mathematical intrigue (see, e.g., [2]). However, it has proven challenging to model distrust propagation in a manner which is both logically consistent and psychologically plausible. We propose a novel and simple extension of the Page Rank equations, and argue that it naturally captures most types of distrust that are expressed in such networks. We give an efficient algorithm for implementing the system and prove desirable properties of the system. 1
Dirichlet PageRank and Trustbased Ranking Algorithms
"... Abstract. Motivated by numerous models of representing trust and distrust within a graph ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. An efficient algorithm is given for computing Dirichlet PageRank vectors subject to Dirichlet bo ..."
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Abstract. Motivated by numerous models of representing trust and distrust within a graph ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. An efficient algorithm is given for computing Dirichlet PageRank vectors subject to Dirichlet boundary conditions on a subset of nodes. We then give several algorithms for various trustbased ranking problems using Dirichlet PageRank with boundary conditions, showing several applications of our algorithms. 1
Dirichlet PageRank and Ranking Algorithms Based on Trust and Distrust
"... Abstract. Motivated by numerous models of representing trust and distrust within a network ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. We propose and analyze a general ranking metric, called Dirichlet PageRank, which gives a ran ..."
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Abstract. Motivated by numerous models of representing trust and distrust within a network ranking system, we examine a quantitative vertex ranking with consideration of the influence of a subset of nodes. We propose and analyze a general ranking metric, called Dirichlet PageRank, which gives a ranking of vertices in a subset S of nodes subject to some specified conditions on the vertex boundary of S. In addition to the usual Dirichlet boundary condition (which disregards the influence of nodes outside of S), we consider general boundary conditions allowing the presence of negative (distrustful) nodes or edges. We give an efficient approximation algorithm for computing Dirichlet PageRank vectors. Furthermore, we give several algorithms for solving various trustbased ranking problems using Dirichlet PageRank with general boundary conditions. 1
G C I
, 2009
"... Sandia is a multiprogram laboratory operated by Sandia Corporation, ..."
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WWW 2009 MADRID! Track: Social Networks and Web 2.0 / Session: Interactions in Social Communities The Slashdot Zoo: Mining a Social Network with Negative Edges
"... christian.bauckhage ..."
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Summary
, 2014
"... Some figures in this document are best viewed in colour. If you received a blackandwhite copy, please consult the online version if necessary. Technical reports published by the University of Cambridge Computer Laboratory are freely available via the Internet: ..."
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Some figures in this document are best viewed in colour. If you received a blackandwhite copy, please consult the online version if necessary. Technical reports published by the University of Cambridge Computer Laboratory are freely available via the Internet:
1Predictable
"... dynamics of opinion forming for networks with antagonistic interactions ..."
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