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Search Results: 1 - 10 of 112041 matches for " Haluk O. Bingol "
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Gossip on Weighted Networks
Mursel Tasgin,Haluk O. Bingol
Physics , 2012, DOI: 10.1142/S0219525912500610
Abstract: We investigate how suitable a weighted network is for gossip spreading. The proposed model is based on the gossip spreading model introduced by Lind et.al. on unweighted networks. Weight represents "friendship." Potential spreader prefers not to spread if the victim of gossip is a "close friend". Gossip spreading is related to the triangles and cascades of triangles. It gives more insight about the structure of a network. We analyze gossip spreading on real weighted networks of human interactions. 6 co-occurrence and 7 social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks and computer generated networks based on ER, BA, WS models are also investigated. They are found to be quite different than the human interaction networks.
Attention Competition with Advertisement
Uzay Cetin,Haluk O. Bingol
Physics , 2012, DOI: 10.1103/PhysRevE.90.032801
Abstract: In the new digital age, information is available in large quantities. Since information consumes primarily the attention of its recipients, the scarcity of attention is becoming the main limiting factor. In this study, we investigate the impact of advertisement pressure on a cultural market where consumers have a limited attention capacity. A model of competition for attention is developed and investigated analytically and by simulation. Advertisement is found to be much more effective when attention capacity of agents is extremely scarce. We have observed that the market share of the advertised item improves if dummy items are introduced to the market while the strength of the advertisement is kept constant.
Iterated Prisoners Dilemma with limited attention
Uzay ?etin,Haluk O. Bingol
Computer Science , 2014, DOI: 10.5488/CMP.17.33001
Abstract: How attention scarcity effects the outcomes of a game? We present our findings on a version of the Iterated Prisoners Dilemma (IPD) game in which players can accept or refuse to play with their partner. We study the memory size effect on determining the right partner to interact with. We investigate the conditions under which the cooperators are more likely to be advantageous than the defectors. This work demonstrates that, in order to beat defection, players do not need a full memorization of each action of all opponents. There exists a critical attention capacity threshold to beat defectors. This threshold depends not only on the ratio of the defectors in the population but also on the attention allocation strategy of the players.
Context Sensitive Article Ranking with Citation Context Analysis
Metin Doslu,Haluk O. Bingol
Computer Science , 2015,
Abstract: It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and weighted citation network based on the target topic. We create a directed and weighted edge between two articles if citation context contains terms related with the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents.
Asymmetries of Men and Women in Selecting Partner
Haluk O. Bingol,Omer Basar
Computer Science , 2012,
Abstract: This paper investigates human dynamics in a large online dating site with 3,000 new users daily who stay in the system for 3 months on the average. The daily activity is also quite large such as 500,000 massage transactions, 5,000 photo uploads, and 20,000 votes. The data investigated has 276, 210 male and 483, 963 female users. Based on the activity that they made, there are clear distinctions between men and women in their pattern of behavior. Men prefer lower, women prefer higher qualifications in their partner.
Fame Emerges as a Result of Small Memory
Haluk Bingol
Physics , 2006, DOI: 10.1103/PhysRevE.77.036118
Abstract: A dynamic memory model is proposed in which an agent ``learns'' a new agent by means of recommendation. The agents can also ``remember'' and ``forget''. The memory size is decreased while the population size is kept constant. ``Fame'' emerged as a few agents become very well known in expense of the majority being completely forgotten. The minimum and the maximum of fame change linearly with the relative memory size. The network properties of the who-knows-who graph, which represents the state of the system, are investigated.
A Cultural Market Model
Amac Herdagdelen,Haluk Bingol
Physics , 2007, DOI: 10.1142/S012918310801208X
Abstract: Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products dramatically depend on consumer heterogeneity and social interaction pressure. Furthermore, the relation between the resulting market shares and social interaction is robust with respect to a wide range of variation in the parameter values and the type of topology.
Community Detection in Complex Networks using Genetic Algorithm
Mursel Tasgin,Haluk Bingol
Physics , 2006,
Abstract: Community structure identification has been an important research topic in complex networks and there has been many algorithms proposed so far to detect community structures in complex networks, where most of the algorithms are not suitable for very large networks because of their time-complexity. Genetic algorithm for detecting communities in complex networks, which is based on optimizing network modularity using genetic algorithm, is presented here. It is scalable to very large networks and does not need any priori knowledge about number of communities or any threshold value. It has O(e) time-complexity where e is the number of edges in the network. Its accuracy is tested with the known Zachary Karate Club and College Football datasets. Enron e-mail dataset is used for scalability test.
Community Detection in Complex Networks Using Agents
Ismail Gunes,Haluk Bingol
Computer Science , 2006,
Abstract: Community structure identification has been one of the most popular research areas in recent years due to its applicability to the wide scale of disciplines. To detect communities in varied topics, there have been many algorithms proposed so far. However, most of them still have some drawbacks to be addressed. In this paper, we present an agent-based based community detection algorithm. The algorithm that is a stochastic one makes use of agents by forcing them to perform biased moves in a smart way. Using the information collected by the traverses of these agents in the network, the network structure is revealed. Also, the network modularity is used for determining the number of communities. Our algorithm removes the need for prior knowledge about the network such as number of the communities or any threshold values. Furthermore, the definite community structure is provided as a result instead of giving some structures requiring further processes. Besides, the computational and time costs are optimized because of using thread like working agents. The algorithm is tested on three network data of different types and sizes named Zachary karate club, college football and political books. For all three networks, the real network structures are identified in almost every run.
Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks
Burak Cetin,Haluk Bingol
Computer Science , 2008,
Abstract: We introduce a family of novel ranking algorithms called ERank which run in linear/near linear time and build on explicitly modeling a network as uncertain evidence. The model uses Probabilistic Argumentation Systems (PAS) which are a combination of probability theory and propositional logic, and also a special case of Dempster-Shafer Theory of Evidence. ERank rapidly generates approximate results for the NP-complete problem involved enabling the use of the technique in large networks. We use a previously introduced PAS model for citation networks generalizing it for all networks. We propose a statistical test to be used for comparing the performances of different ranking algorithms based on a clustering validity test. Our experimentation using this test on a real-world network shows ERank to have the best performance in comparison to well-known algorithms including PageRank, closeness, and betweenness.
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