Browsing by Subject "Centrality"
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Item Determining influence in social networks using Social Capital(2014-05) Sharma, DhruvThe proliferation of online social networks enables the influence of a person or an event to propagate to every corner of the globe in a very short duration of time. The problem of identifying such key sources of influence is important for a wide variety of applications from sales and marketing to public health and policies. Most of the existing methods for identifying influencers use the process of information diffusion to discover the nodes (people) with the maximum expected information spread. In this work we have developed a novel method for identifying key influencers in a given network. This method works on the premise that people generate more value for their work by collaborating with peers having high influence. The social value generated through such collaborations denotes the notion of individual social capital. At the core of this method we use the popular valuation-allocation approach for finding the individual social capital value. In this approach first we determine the value of the entire network using a valuation function and then we do a fair allocation of this entire network's value amongst the participating nodes (people). We show that our Valuation and Allocation functions satisfy several axioms of fairness and fall under the Myerson's allocation rule class.Also, we implement our allocation rule using an efficient algorithm and show that our algorithm outperforms the baselines in several real life datasets. Especially, for the DBLP collaboration network our algorithm outperforms PageRank, PMIA and Weighted Degree baselines by up to 8% in terms of precision recall and F1-measure.Furthermore, we use Hypergraphs as a tool to model group collaborations more effectively and empirically show the superiority of hypergraph edge weights as compared to dyadic edge weights for identifying influencers.To conclude with we discuss a couple of popular distributed programming paradigms, namely MapReduce and BSP (Bulk Synchronous Parallel) and the implementation of the algorithm on these.Item ‘New urbanism’ or metropolitan-level centralization? A comparison of the influences of metropolitan-level and neighborhood-level urban form characteristics on travel behavior(Journal of Transport and Land Use, 2011) Naess, PetterBased on a study in the Copenhagen Metropolitan Area, this paper compares the influences of macro-level and micro-level urban form characteristics on the respondents’ traveling distance by car on weekdays. The Copenhagen study shows that metropolitan-scale urban- structural variables generally exert stronger influences than neighborhood-scale built-environment characteristics on the amount of car travel. In particular, the location of the residence relative to the main city center of the metropolitan region shows a strong effect. Some local scale variables often described as influential in the literature, such as neighborhood street pattern, show no significant effect on car travel when provisions are made to control for the location of the dwelling relative to the city center.Item Price Discrimination in Large Social Networks(2021-05) Huang, JialiRecent trends point to increasing use of social network information by firms and public agencies for personalized policies. However, the cost of implementation can be high and the use of personal information can reduce satisfaction. The value of such policies depends upon network structures, and may be insignificant for certain classes of large networks. Thus, firms and public agencies may need to be more careful about the design of mechanisms within social networks. In this thesis, we focus on a particular application of mechanism design problems with social network effects, i.e., the pricing problem of a single firm selling a product to consumers in social networks, and study the value of price discrimination in large social networks. Recent trends in industry suggest that increasingly firms are using information about social network to offer personalized prices to individuals based upon their positions in the social network. In the presence of positive network externalities, firms aim to increase their profits by offering discounts to influential individuals that can stimulate consumption by other individuals at a higher price. However, the lack of transparency in discriminative pricing may reduce consumer satisfaction and create mistrust. Recent research has focused on the computation of optimal prices in deterministic networks under positive externalities. We would like to answer the question: how valuable is such discriminative pricing? We find, surprisingly, that the value of such pricing policies (increase in profits due to price discrimination) in very large random networks are often not significant. Particularly, for Erd\"{o}s-Renyi random networks, we provide the exact rates at which this value decays in the size of the networks for different ranges of network densities. Our results show that there is a non-negligible value of price discrimination for a small class of moderate-sized Erd\"{o}s-Renyi random networks. We also present a framework to obtain bounds on the value of price discrimination for random networks with general degree distributions and apply the framework to obtain bounds on the value of price discrimination in power-law networks. Our numerical experiments demonstrate our results and suggest that our results are robust to changes in the model of network externalities.