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Incremental Algorithms for Network Management and Analysis based on Closeness Centrality
, 1303
"... Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centralitybased network management. The problem has many applicat ..."
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Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centralitybased network management. The problem has many applications such as verifying the robustness of the networks and controlling or improving the entity dissemination. It can be defined as finding a small set of topological network modifications which yield a desired closeness centrality configuration. As a fundamental building block to tackle that problem, we propose incremental algorithms which efficiently update the closeness centrality values upon changes in network topology, i.e., edge insertions and deletions. Our algorithms are proven to be efficient on many reallife networks, especially on smallworld networks, which have a small diameter and a spikeshaped shortest distance distribution. In addition to closeness centrality, they can also be a great arsenal for the shortestpathbased management and analysis of the networks. We experimentally validate the efficiency of our algorithms on large networks and show that they update the closeness centrality values of the temporal DBLPcoauthorship network of 1.2 million users 460 times faster than it would take to compute them from scratch. To the best of our knowledge, this is the first work which can yield practical largescale network management based on closeness centrality values.
Exploring social network effects on popularity biases in
"... recommender systems ..."
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Possible and Necessary Winner Problem in Social Polls
"... Social networks are increasingly being used to conduct polls. We introduce a simple model of such social polling. We suppose agents vote sequentially, but the order in which agents choose to vote is not necessarily fixed. We also suppose that an agent’s vote is influenced by the votes of their frien ..."
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Social networks are increasingly being used to conduct polls. We introduce a simple model of such social polling. We suppose agents vote sequentially, but the order in which agents choose to vote is not necessarily fixed. We also suppose that an agent’s vote is influenced by the votes of their friends who have already voted. Despite its simplicity, this model provides useful insights into a number of areas including social polling, sequential voting, and manipulation. We prove that the number of candidates and the network structure affect the computational complexity of computing which candidate necessarily or possibly can win in such a social poll. For social networks with bounded treewidth and a bounded number of candidates, we provide polynomial algorithms for both problems. In other cases, we prove that computing which candidates necessarily or possibly win are computationally intractable.
Competence Fields as a Means of Establishing Political Leadership
"... „Trust me, I know what I'm doing!“ ..."
SCALE EVENTS By
, 2014
"... The diffusion of information through population affects how and when the public reacts in various situations. Thus, it is important to understand how and at what speed important information spreads. Social media platforms are important to track and understand such diffusion. Twitter provides a conve ..."
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The diffusion of information through population affects how and when the public reacts in various situations. Thus, it is important to understand how and at what speed important information spreads. Social media platforms are important to track and understand such diffusion. Twitter provides a convenient and effective way to measure it. This study used data obtained from 15,000 Twitter users. Data was collected on the following events: Hurricane Irene, Hurricane Sandy, Osama Bin Laden's capture, and the United States ’ 2012 Presidential Election. Information such as the time of a tweet, the user name, content, and the ID was analyzed to measure the diffusion of information and track the trajectory of retweets. The spread of information was visualized and analyzed to determine how far and how fast the information spread. The results show how information spreads and the content