8,258 research outputs found

    Measuring social dynamics in a massive multiplayer online game

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    Quantification of human group-behavior has so far defied an empirical, falsifiable approach. This is due to tremendous difficulties in data acquisition of social systems. Massive multiplayer online games (MMOG) provide a fascinating new way of observing hundreds of thousands of simultaneously socially interacting individuals engaged in virtual economic activities. We have compiled a data set consisting of practically all actions of all players over a period of three years from a MMOG played by 300,000 people. This large-scale data set of a socio-economic unit contains all social and economic data from a single and coherent source. Players have to generate a virtual income through economic activities to `survive' and are typically engaged in a multitude of social activities offered within the game. Our analysis of high-frequency log files focuses on three types of social networks, and tests a series of social-dynamics hypotheses. In particular we study the structure and dynamics of friend-, enemy- and communication networks. We find striking differences in topological structure between positive (friend) and negative (enemy) tie networks. All networks confirm the recently observed phenomenon of network densification. We propose two approximate social laws in communication networks, the first expressing betweenness centrality as the inverse square of the overlap, the second relating communication strength to the cube of the overlap. These empirical laws provide strong quantitative evidence for the Weak ties hypothesis of Granovetter. Further, the analysis of triad significance profiles validates well-established assertions from social balance theory. We find overrepresentation (underrepresentation) of complete (incomplete) triads in networks of positive ties, and vice versa for networks of negative ties...Comment: 23 pages 19 figure

    Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Objectives

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    The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by introducing redundancies. They also utilize a crossover operator which imposes a linear ordering that is not suitable for community detection. The algorithm presented here is a framework to detect communities for complex biological networks that removes both redundancies and linearity. We also introduce a novel operator, named Gene Repair. This algorithm is unique as it is a flexible community detection technique aimed at maximizing the value of any given mathematical objective for the network. We reduce the memory requirements by representing chromosomes as a 3-dimensional bit array. Furthermore, in order to increase diversity while retaining promising chromosomes, we use natural selection process based on tournament selection with elitism. Additionally, our approach doesn’t require prior information about the number of true communities in the network. We apply our novel algorithm to benchmark datasets and also to a network representing a large cohort of AD cases and controls. By utilizing this efficient and flexible implementation that is cognizant of characteristics for networks representing complex disease genetics, we sift out communities representing patterns of interacting genetic variants that are associated with this enigmatic disease

    Dagstuhl Reports : Volume 1, Issue 2, February 2011

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    Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

    Signed Network Modeling Based on Structural Balance Theory

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    The modeling of networks, specifically generative models, have been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. Recently there has been a considerable increase in interest for the better understanding and modeling of networks, but the vast majority of this work has been for unsigned networks. However, many networks can have positive and negative links(or signed networks), especially in online social media, and they inherently have properties not found in unsigned networks due to the added complexity. Specifically, the positive to negative link ratio and the distribution of signed triangles in the networks are properties that are unique to signed networks and would need to be explicitly modeled. This is because their underlying dynamics are not random, but controlled by social theories, such as Structural Balance Theory, which loosely states that users in social networks will prefer triadic relations that involve less tension. Therefore, we propose a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu model for the modeling of signed networks. Our model introduces two parameters that are able to help maintain the positive link ratio and proportion of balanced triangles. Empirical experiments on three real-world signed networks demonstrate the importance of designing models specific to signed networks based on social theories to obtain better performance in maintaining signed network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174

    Predicting Multi-actor collaborations using Hypergraphs

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    Social networks are now ubiquitous and most of them contain interactions involving multiple actors (groups) like author collaborations, teams or emails in an organizations, etc. Hypergraphs are natural structures to effectively capture multi-actor interactions which conventional dyadic graphs fail to capture. In this work the problem of predicting collaborations is addressed while modeling the collaboration network as a hypergraph network. The problem of predicting future multi-actor collaboration is mapped to hyperedge prediction problem. Given that the higher order edge prediction is an inherently hard problem, in this work we restrict to the task of predicting edges (collaborations) that have already been observed in past. In this work, we propose a novel use of hyperincidence temporal tensors to capture time varying hypergraphs and provides a tensor decomposition based prediction algorithm. We quantitatively compare the performance of the hypergraphs based approach with the conventional dyadic graph based approach. Our hypothesis that hypergraphs preserve the information that simple graphs destroy is corroborated by experiments using author collaboration network from the DBLP dataset. Our results demonstrate the strength of hypergraph based approach to predict higher order collaborations (size>4) which is very difficult using dyadic graph based approach. Moreover, while predicting collaborations of size>2 hypergraphs in most cases provide better results with an average increase of approx. 45% in F-Score for different sizes = {3,4,5,6,7}

    Using NEAT for Continuous Adaptation and Teamwork Formation in Pacman

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    Despite games often being used as a testbed for new computational intelligence techniques, the majority of artificial intelligence in commercial games is scripted. This means that the computer agents are non-adaptive and often inherently exploitable because of it. In this paper, we describe a learning system designed for team strategy development in a real time multi-agent domain. We test our system in the game of Pacman, evolving adaptive strategies for the ghosts in simulated real time against a competent Pacman player. Our agents (the ghosts) are controlled by neural networks, whose weights and structure are incrementally evolved via an implementation of the NEAT (Neuro-Evolution of Augmenting Topologies) algorithm. We demonstrate the design and successful implementation of this system by evolving a number of interesting and complex team strategies that outperform the ghosts\u27 strategies of the original arcade version of the game

    Mimicking human player strategies in fighting games using game artificial intelligence techniques

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    Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. However, there are two issues with these approaches. First, the single player offering in many fighting games is regarded as being simplistic in design, making the moves by the computer predictable. Secondly, while playing against other human players can be more varied and challenging, this may not always be achievable due to the logistics involved in setting up such a bout. Game Artificial Intelligence could provide a solution to both of these issues, allowing a human player s strategy to be learned and then mimicked by the AI fighter. In this thesis, game AI techniques have been researched to provide a means of mimicking human player strategies in strategic fighting games with multiple parameters. Various techniques and their current usages are surveyed, informing the design of two separate solutions to this problem. The first solution relies solely on leveraging k nearest neighbour classification to identify which move should be executed based on the in-game parameters, resulting in decisions being made at the operational level and being fed from the bottom-up to the strategic level. The second solution utilises a number of existing Artificial Intelligence techniques, including data driven finite state machines, hierarchical clustering and k nearest neighbour classification, in an architecture that makes decisions at the strategic level and feeds them from the top-down to the operational level, resulting in the execution of moves. This design is underpinned by a novel algorithm to aid the mimicking process, which is used to identify patterns and strategies within data collated during bouts between two human players. Both solutions are evaluated quantitatively and qualitatively. A conclusion summarising the findings, as well as future work, is provided. The conclusions highlight the fact that both solutions are proficient in mimicking human strategies, but each has its own strengths depending on the type of strategy played out by the human. More structured, methodical strategies are better mimicked by the data driven finite state machine hybrid architecture, whereas the k nearest neighbour approach is better suited to tactical approaches, or even random button bashing that does not always conform to a pre-defined strategy
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