SOCNET 2018 - Proceedings of the “Second International Workshop on Modeling, Analysis, and Management of Social Networks and Their Applications”

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.Die Modellierung, Analyse, Steuerung und das Management komplexer sozialer Netzwerke repräsentiert einen bedeutsamen Bereich interdisziplinärer Forschung in einer modernen digitalisierten Welt. Im letzten Jahrzehnt haben soziale Netzwerke wichtige Online Anwendungen hervorgebracht, die auf einer modernen Internet-Infrastruktur ablaufen und als eine Hauptquelle des rasant anwachsenden Internetverkehrs identifiziert wurden. Der zweite internationale Workshop "Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) wurde am 28. Februar 2018 an der Friedrich-Alexander-Universität Erlangen-Nürnberg abgehalten und stellte Forschungsergebnisse zu sozialen Netzwerken in einer modernen Informationsgesellschaft vor. Die SOCNET 2018 Proceedings stellen die Themen eines Tutoriums "Network Analysis in Python" heraus, präsentieren einen eingeladenen Beitrag "From the Age of Emperors to the Age of Empathy" und fassen die Ergebnisse von acht begutachteten wissenschaftlichen Beiträgen zusammen. Die abgedeckten Themen reichen von theoretisch ausgerichteten Studien zur Strukturanalyse thematischer Netzwerke, der Modellierung von Gruppendynamik sowie der Netzwerkanalyse von Rettungseinsätzen bis zu den Anwendungsbereichen sozialer Netzwerke, z.B. der Nutzung sozialer Medien in Organisationen sowie der Wirkungsanalyse sozialer Netzwerkanwendungen in modernen Informationsgesellschaften. Die SOCNET 2018 Proceedings sollen die Leser zu neuen Forschungen im Bereich der Messung, Modellierung und Analyse sozialer Netzwerke anregen und sie zur Entwicklung neuer sozialer Netzwerkapplikationen der nächsten Generation auffordern

A survey on the analysis and control of evolutionary matrix games

In support of the growing interest in how to efficiently influence complex systems of interacting self interested agents, we present this review of fundamental concepts, emerging research, and open problems related to the analysis and control of evolutionary matrix games, with particular emphasis on applications in social, economic, and biological networks. (C) 2018 Elsevier Ltd. All rights reserved

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

Parametric controllability of the personalized PageRank: Classic model vs biplex approach

[EN] Measures of centrality in networks defined by means of matrix algebra, like PageRank-type centralities, have been used for over 70 years. Recently, new extensions of PageRank have been formulated and may include a personalization (or teleportation) vector. It is accepted that one of the key issues for any centrality measure formulation is to what extent someone can control its variability. In this paper, we compare the limits of variability of two centrality measures for complex networks that we call classic PageRank (PR) and biplex approach PageRank (BPR). Both centrality measures depend on the so-called damping parameter alpha that controls the quantity of teleportation. Our first result is that the intersection of the intervals of variation of both centrality measures is always a nonempty set. Our second result is that when alpha is lower that 0.48 (and, therefore, the ranking is highly affected by teleportation effects) then the upper limits of PR are more controllable than the upper limits of BPR; on the contrary, when alpha is greater than 0.5 (and we recall that the usual PageRank algorithm uses the value 0.85), then the upper limits of PR are less controllable than the upper limits of BPR, provided certain mild assumptions on the local structure of the graph. Regarding the lower limits of variability, we give a result for small values of alpha. We illustrate the results with some analytical networks and also with a real Facebook network.This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities under Project Nos. PGC2018-101625-B-I00, MTM2016-76808-P, and MTM2017-84194-P (AEI/FEDER, UE).Flores, J.; García, E.; Pedroche Sánchez, F.; Romance, M. (2020). 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