Results 11  20
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34
Analyzing Protein Interaction Networks via Random Graph Model
, 2005
"... Many complex systems may best be described as networks, which we can use graph theory to analyze their topological properties. In an organism, proteinprotein interactions may also be mapped into complex network. Here we use random graph theory to analyze seven different organism protein interaction ..."
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Many complex systems may best be described as networks, which we can use graph theory to analyze their topological properties. In an organism, proteinprotein interactions may also be mapped into complex network. Here we use random graph theory to analyze seven different organism protein interaction networks. Three topological properties (degree distribution, clustering coefficient and average shortest path) were used to characterize these networks. The logarithm of the node degree distribution vs. the logarithm of the node degree plot shows that all seven species follow a powerlaw distribution quite well. In addition, we also obtained the relatively high clustering coefficient of these protein interaction networks. The distance between two nodes of these protein interaction networks indicates that it is quite short comparing with the large network size. The plot of the logarithm of the frequency vs. the shortest path length also indicates that the shortest path length distribution follows a
Local learning to improve organizational performance in networked multiagent team formation
 In Proceedings of the AAAI 05 Workshop on MultiAgent Learning
, 2005
"... Networked multiagent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply chain networks and sensor networks. A common challenge in many networked multiagent systems is decentralized team formation among the ..."
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Networked multiagent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply chain networks and sensor networks. A common challenge in many networked multiagent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multiagent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multiagent team formation in networked multiagent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a naive heuristic.
Graphlet decomposition of a weighted network
"... We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable algorithm, which combines EM with BronKerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one n ..."
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We introduce the graphlet decomposition of a weighted network, which encodes a notion of social information based on social structure. We develop a scalable algorithm, which combines EM with BronKerbosch in a novel fashion, for estimating the parameters of the model underlying graphlets using one network sample. We explore theoretical properties of graphlets, including computational complexity, redundancy and expected accuracy. We test graphlets on synthetic data, and we analyze messaging on Facebook and crime associations in the 19th century. 1
Measuring generalized preferential attachment in dynamic social networks
, 2005
"... The mechanism of preferential attachment underpins most recent social network formation models. Yet few authors attempt to check or quantify assumptions on this mechanism. We call generalized preferential attachment any kind of preferential interaction behavior with respect to any node property, and ..."
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The mechanism of preferential attachment underpins most recent social network formation models. Yet few authors attempt to check or quantify assumptions on this mechanism. We call generalized preferential attachment any kind of preferential interaction behavior with respect to any node property, and introduce tools for measuring it empirically. We then apply them to the sociosemantic network of scientific collaborations, investigating in particular homophilic behavior. Characterizing comprehensively such phenomena opens the way to a whole class of realistic and credible social network morphogenesis models.
Vertex routing models
, 906
"... Abstract. A class of models describing the flow of information within networks via routing processes is proposed and investigated, concentrating on the effects of memory traces on the global properties. The longterm flow of information is governed by cyclic attractors, allowing to define a measure ..."
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Abstract. A class of models describing the flow of information within networks via routing processes is proposed and investigated, concentrating on the effects of memory traces on the global properties. The longterm flow of information is governed by cyclic attractors, allowing to define a measure for the information centrality of a vertex given by the number of attractors passing through this vertex. We find the number of vertices having a nonzero information centrality to be extensive/subextensive for models with/without a memory trace in the thermodynamic limit. We evaluate the distribution of the number of cycles, of the cycle length and of the maximal basins of attraction, finding a complete scaling collapse in the thermodynamic limit for the latter. Possible implications of our results on the information flow in social networks are discussed. PACS numbers: 89.75.Hc, 02.10.Ox, 87.23.Ge, 89.75.FbVertex routing models 2 1.
Network Algorithmics and the Emergence of the Cortical SynapticWeight Distribution
, 906
"... When a neuron fires and the resulting action potential travels down its axon toward other neurons ’ dendrites, the effect on each of those neurons is mediated by the weight of the synapse that separates it from the firing neuron. This weight, in turn, is affected by the postsynaptic neuron’s respons ..."
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When a neuron fires and the resulting action potential travels down its axon toward other neurons ’ dendrites, the effect on each of those neurons is mediated by the weight of the synapse that separates it from the firing neuron. This weight, in turn, is affected by the postsynaptic neuron’s response through a mechanism that is thought to underlie important processes such as learning and memory. Although of difficult quantification, cortical synaptic weights have been found to obey a longtailed unimodal distribution peaking near the lowest values, thus confirming some of the predictive models built previously. These models are all causally local, in the sense that they refer to the situation in which a number of neurons all fire directly at the same postsynaptic neuron. Consequently, they necessarily embody assumptions regarding the generation of action potentials by the presynaptic neurons that have little biological interpretability. In this letter we introduce a network model of large groups of interconnected neurons and demonstrate, making none of the assumptions that characterize the causally local models, that its longterm behavior gives rise to a distribution of synaptic weights with the same properties that were experimentally observed. In our model the action potentials that create a neuron’s input are, ultimately, the product of networkwide causal chains relating what happens at a neuron to the firings of others. Our model is then of a causally global nature and predicates the emergence of the synapticweight distribution on network structure and function. As such, it has the potential to become instrumental also in the study of other emergent cortical phenomena.
NearOptimal Solutions and Large Integrality Gaps for Almost All Instances of SingleMachine PrecedenceConstrained Scheduling
, 2010
"... We consider the problem of minimizing the weighted sum of completion times on a single machine subject to bipartite precedence constraints where all minimal jobs have unit processing time and zero weight, and all maximal jobs have zero processing time and unit weight. For various probability distrib ..."
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We consider the problem of minimizing the weighted sum of completion times on a single machine subject to bipartite precedence constraints where all minimal jobs have unit processing time and zero weight, and all maximal jobs have zero processing time and unit weight. For various probability distributions over these instances—including the uniform distribution—we show several “almost all”type results. First, we show that almost all instances are prime with respect to a wellstudied decomposition for this scheduling problem. Second, we show that for almost all instances, every feasible schedule is arbitrarily close to optimal. Finally, for almost all instances, we give a lower bound on the integrality gap of various linear programming relaxations of this problem.
Submitted to Decision Support Systems manuscript DSS 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. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. 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 an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company 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. The simple outdegree centrality performed well in all treatments. Key words: customer relationship management, viral marketing, centrality, network theory, word of mouth marketing