A Cultural Market Model

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

Use of Rapid Probabilistic Argumentation for Ranking on Large Complex Networks

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.Comment: 11 pages, 10 figure

Context Sensitive Article Ranking with Citation Context Analysis

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

Compatibility of Mating Preferences

Human mating is a complex phenomenon. Although men and women have different preferences in mate selection, there should be compatibility in these preferences since human mating requires agreement of both parties. We investigate how compatible the mating preferences of men and women are in a given property such as age, height, education and income. We use dataset of a large online dating site (N = 44, 255 users). (i) Our findings are based on the "actual behavior" of users trying to find a date online, rather than questions about a "hypothetical" partner as in surveys. (ii) We confirm that women and men have different mating preferences. Women prefer taller and older men with better education and higher income then themselves. Men prefer just the opposite. (iii) Our findings indicate that these differences complement each other. (iv) Highest compatibility is observed in income with 95 %. This might be an indication that income is in the process of becoming more important than other properties, including age, in our modern society. (v) An evolutionary model is developed which produces similar results.Comment: 8 pages, 3 figure

Asymmetries of Men and Women in Selecting Partner

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

Computational Models for Commercial Advertisements in Social Networks

Identifying noteworthy spreaders in a network is essential for understanding the spreading process and controlling the reach of the spread in the network. The nodes that are holding more intrinsic power to extend the reach of the spread are important due to demand for various applications such as viral marketing, controlling rumor spreading or get a better understanding of spreading of the diseases. As an application of the viral marketing, maximization of the reach with a fixed budget is a fundamental requirement in the advertising business. Distributing a fixed number of promotional items for maximizing the viral reach can leverage influencer detection methods. For detecting such "influencer" nodes, there are local metrics such as degree centrality (mostly used as in-degree centrality) or global metrics such as k-shell decomposition or eigenvector centrality. All the methods can rank graphs but they all have limitations and there is still no de-facto method for influencer detection in the domain. In this paper, we propose an extended k-shell algorithm which better utilizes the k-shell decomposition for identifying viral spreader nodes using the topological features of the network. We use Susceptible-Infected-Recovered model for the simulations of the spreading process in real-life networks and the simulations demonstrates that our approach can reach to up to 36% larger crowds within the same network, with the same number of initial spreaders

An Accelerometer Based Calculator for Visually Impaired People Using Mobile Devices

Recent trend of touch-screen devices produces an accessibility barrier for visually impaired people. On the other hand, these devices come with sensors such as accelerometer. This calls for new approaches to human computer interface (HCI). In this study, our aim is to find an alternative approach to classify 20 different hand gestures captured by iPhone 3GS's built-in accelerometer and make high accuracy on user-independent classifications using Dynamic Time Warping (DTW) with dynamic warping window sizes. 20 gestures with 1,100 gesture data are collected from 15 normal-visioned people. This data set is used for training. Experiment-1 based on this data set produced an accuracy rate of 96.7~\%. In order for visually impaired people to use the system, a gesture recognition based "talking" calculator is implemented. In Experiment-2, 4 visually impaired end-users used the calculator and obtained 95.5~\% accuracy rate among 17 gestures with 720 gesture data totally. Contributions of the techniques to the end result is also investigated. Dynamic warping window size is found to be the most effective one. The data and the code is available

The Dose of the Threat Makes the Resistance for Cooperation

We propose to reformulate the payoff matrix structure of Prisoner's Dilemma Game, by introducing threat and greed factors, and show their effect on the co-evolution of memory and cooperation. Our findings are as follows. (i) Memory protects cooperation. (ii) To our surprise, greater memory size is unfavorable to evolutionary success when there is no threat. In the absence of threat, subsequent generations lose their memory and are consequently invaded by defectors. (iii) In contrast, the presence of an appropriate level of threat triggers the emergence of a self-protection mechanism for cooperation, which manifests itself as an increase in memory size within subsequent generations. On the evolutionary level, memory size acts like an immune response of the generations against aggressive defection. (iv) Even more extreme threat results again in defection. Our findings boil down to the following: The dose of the threat makes the resistance for cooperation

Community detection using preference networks

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it can not run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.Comment: 11 pages, 11 figures, 2 tables and supplementary informatio

Getting recommendation is not always better

We present an extended version of the Iterated Prisoner's Dilemma game in which agents with limited memory receive recommendations about the unknown opponent to decide whether to play with. Since agents can receive more than one recommendations about the same opponent, they have to evaluate the recommendations according to their disposition such as optimist, pessimist, or realist. They keep their firsthand experience in their memory. Since agents have limited memory, they have to use different forgetting strategies. Our results show that getting recommendations not always perform better. We observe that realist performs the best and optimist the worse.Comment: 9 pages, 5 figure