Enhancing digital business ecosystem trust and reputation with centrality measures

Digital Business Ecosystem (DBE) is a decentralised environment where very small enterprises (VSEs) and small to medium sized enterprises (SMEs) interoperate by establishing collaborations with each other. Collaborations play a major role in the development of DBEs where it is often difficult to select partners, as they are most likely strangers. Even though trust forms the basis for collaboration decisions, trust and reputation information may not be available for each participant. Recommendations from other participants are therefore necessary to help with the selection process. Given the nature of DBEs, social network centrality measures that can influence power and control in the network need to be considered for DBE trust and reputation. A number of social network centralities, which influence reputation in social graphs have been studied in the past. This paper investigates an unexploited centrality measure, betweenness centrality, as a metric to be considered for trust and reputation

Enhancing digital business ecosystem trust and reputation with centrality measures

Digital Business Ecosystem (DBE) is a decentralised environment where very small enterprises (VSEs) and small to medium sized enterprises (SMEs) interoperate by establishing collaborations with each other. Collaborations play a major role in the development of DBEs where it is often difficult to select partners, as they are most likely strangers. Even though trust forms the basis for collaboration decisions, trust and reputation information may not be available for each participant. Recommendations from other participants are therefore necessary to help with the selection process. Given the nature of DBEs, social network centrality measures that can influence power and control in the network need to be considered for DBE trust and reputation. A number of social network centralities, which influence reputation in social graphs have been studied in the past. This paper investigates an unexploited centrality measure, betweenness centrality, as a metric to be considered for trust and reputation

Capital social y confianza social: la respuesta del Estado ante la propagación de COVID-19 en Indonesia

The main purpose of this study was to analyze the steps taken by the Indonesian government to implement different policies for its fight against COVID-19. The Indonesian government has taken various measures, most pertinently Large-Scale Social Restrictions (PSBB), to reduce the spread of the COVID-19 cases. This study used a qualitative approach as a contextual study that sought to emphasize the meaning of a phenomenon in a COVID-19 pandemic situation. The purpose of qualitative research was to make a complex picture. Data analyzed were obtained from various sources, including the internet, such as online media and social media, and related literature.  Data were collected utilizing the Ncapture for the Nvivo feature. The time taken for analysis was from March to May 2020. In conducting the analysis, this study also used Nvivo 12 Plus software. This research reveals that the government policies, most importantly Large-Scale Social Restrictions (PSBB), triggered a decline in the public confidence. The increase in the number of positive cases of COVID-19 in various regions up to May 2020 has made government policies deemed not running optimally. Further, the crisis of confidence affects the community’s participatory pattern in combating the spread of the COVID-19 pandemic in Indonesia. Findings from this research suggest the need for a study on how government policies can work well and also supported by the public trust.l objetivo principal de este estudio fue analizar los pasos dados por el gobierno de Indonesia para implementar diferentes políticas para su lucha contra el COVID-19. El gobierno de Indonesia ha tomado varias medidas, la más pertinente fueron las Restricciones sociales a gran escala (PSBB), para reducir la propagación de los casos de COVID-19. Este estudio utilizó un enfoque cualitativo como un estudio contextual que buscaba enfatizar el significado de un fenómeno en una situación de pandemia de COVID-19. El propósito de la investigación cualitativa era crear una imagen compleja. Los datos analizados se obtuvieron de diversas fuentes, incluida Internet, como los medios en línea y las redes sociales, y la literatura relacionada. Los datos se recopilaron utilizando Ncapture para la función Nvivo. El tiempo necesario para el análisis fue de marzo a mayo de 2020. Para realizar el análisis, este estudio también utilizó el software Nvivo 12 Plus. Esta investigación revela que las políticas gubernamentales, sobre todo las Restricciones Sociales a Gran Escala (PSBB), provocaron una disminución en la confianza pública. El aumento en el número de casos positivos de COVID-19 en varias regiones hasta mayo de 2020 ha hecho que las políticas gubernamentales consideradas no estén funcionando de manera óptima. Además, la crisis de confianza afecta el patrón de participación de la comunidad en la lucha contra la propagación de la pandemia de COVID-19 en Indonesia. Los hallazgos de esta investigación sugieren la necesidad de un estudio sobre cómo las políticas gubernamentales pueden funcionar bien y también respaldadas por la confianza públic

BFF: A Tool for Eliciting Tie Strength and User Communities in Social Networking Services

López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014 Abstract The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength

BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. This article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering - TEE Project.López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014). BFF: A tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers. 16:225-237. https://doi.org/10.1007/s10796-013-9453-6S22523716Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.Boyd, D., & Hargittai, E. (2010). Facebook privacy settings: who cares? First Monday, 15(8).Burt, R. (1995). Structural holes: the social structure of competition. Harvard University Pr.Culotta, A., Bekkerman, R., McCallum, A. (2004). Extracting social networks and contact information from email and the web.Ellison, N., Steinfield, C., Lampe, C. (2007). The benefits of facebook friends: social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.Fang, L., & LeFevre, K. (2010). Privacy wizards for social networking sites. In Proceedings of the 19th international conference on World wide web (pp. 351–360). ACM.Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174.Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the 27th international conference on human factors in computing systems (pp. 211–220). ACM.Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Science, 99(12), 7821.Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 1360–1380.Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM workshop on privacy in the electronic society (pp. 71–80). ACM.Johnson, M., Egelman, S., Bellovin, S. (2012). Facebook and privacy: it’s complicated. In Proceedings of the eighth symposium on usable privacy and security (p. 9). ACM .Kahanda, I., & Neville, J. (2009). Using transactional information to predict link strength in online social networks. In Proceedings of the third international conference on weblogs and social media (ICWSM).Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: a comparative analysis. Physical Review E, 80, 056–117.Lancichinetti, A., Fortunato, S., Kertsz, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3), 033–015.Lin, N., Ensel, W., Vaughn, J. (1981). Social resources and strength of ties: Structural factors in occupational status attainment. American Sociological Review, 393–405.Lipford, H., Besmer, A., Watson, J. (2008). Understanding privacy settings in facebook with an audience view. In Proceedings of the 1st conference on usability, psychology, and security (pp. 1–8). Berkeley: USENIX Association.Liu, G., Wang, Y., Orgun, M. (2010). Optimal social trust path selection in complex social networks. In Proceedings of the 24th AAAI conference on artificial intelligence (pp. 139–1398). AAAI.Matsuo, Y., Mori, J., Hamasaki, M., Nishimura, T., Takeda, H., Hasida, K., Ishizuka, M. (2007). Polyphonet: an advanced social network extraction system from the web. Web Semantics: Science, Services and Agents on the World Wide Web, 5(4), 262–278. World Wide Web Conference 2006 Semantic Web Track.Murukannaiah, P., & Singh, M. (2011). Platys social: relating shared places and private social circles. Internet Computing IEEE, 99, 1–1.Quercia, D., Lambiotte, R., Kosinski, M., Stillwell, D., Crowcroft, J. (2012). The personality of popular facebook users. In Proceedings of the ACM 2012 conference on computer supported cooperative work (CSCW’12).Rosvall, M., & Bergstrom, C. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118–1123.Sharma, G., Qiang, Y., Wenjun, S., Qi, L. (2013). Communication in virtual world: Second life and business opportunities. Information Systems Frontiers, 15(4), 677–694.Shen, K., Song, L., Yang, X., Zhang, W. (2010). A hierarchical diffusion algorithm for community detection in social networks. In 2010 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC) (pp. 276–283). IEEE.Sierra, C., & Debenham, J. (2007). The LOGIC negotiation model. In AAMAS ’07: proceedings of the 6th international joint conference on autonomous agents and multiagent systems (pp. 1–8). ACM.Staddon, J., Huffaker, D., Brown, L., Sedley, A. (2012). Are privacy concerns a turn-off?: engagement and privacy in social networks. In Proceedings of the eighth symposium on usable privacy and security (p. 10). ACM.Strater, K., & Lipford, H.R. (2008). Strategies and struggles with privacy in an online social networking community. In Proceedings of the 22nd British HCI group annual conference on people and computers: culture, creativity, interaction, BCS-HCI ’08 (Vol. 1, pp. 111–119). Swinton: British Computer Society.Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology, 558–588.Wiese, J., Kelley, P., Cranor, L., Dabbish, L., Hong, J., Zimmerman, J. (2011). Are you close with me? are you nearby? investigating social groups, closeness, and willingness to share. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 197–206). ACM.Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. In Proceedings of the 19th international conference on World wide web (pp. 981–990). ACM

Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H-OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.16 page(s

Εξερευνώντας μονοπάτια εμπιστοσύνης

Στα πλαίσια αυτής της διπλωματικής εργασίας μελετήσαμε την έννοια της εμπιστοσύνης στους διαδικτυακούς τόπους, καθώς και τα μοντέλα που έχουν αναπτυχθεί σε διαφορετικούς τύπους δικτύων και ασχολούνται με τον τρόπο διάδοσης της εμπιστοσύνης σε αυτά. Στη συνέχεια αναλύσαμε και τοποθετήσαμε τις διαφορετικές μεθόδους κάτω από ένα ενιαίο πλαίσιο μοντελοποίησης ακολουθώντας τους φορμαλισμούς και τις έννοιες της άλγεβρας μονοπατιών. Στα πειράματα που πραγματοποιήσαμε, επιλέξαμε τρεις αλγορίθμους διαφορετικών μοντέλων εμπιστοσύνης και τους υλοποιήσαμε, τόσο στην αρχική τους μορφή όσο και με βάση τη δική μας προτεινόμενη μοντελοποίηση, για να τα συγκρίνουμε ως προς την ταχύτητα στο δίκτυο Epinions. Τα τρία αυτά μοντέλα χρησιμοποιήθηκαν περαιτέρω και για την πραγματοποίηση έρευνας που διενεργήθηκε με ερευνητές που εργάζονται στο τμήμα Πληροφορικής και Τηλεπικοινωνιών του ΕΚΠΑ. Και στα δύο δίκτυα έγιναν πειράματα εκτίμησης λάθους πρόβλεψης εμπιστοσύνης των τριών μοντέλων για να γίνει εφικτή η ποιοτική σύγκριση του μηχανισμού μετάδοσης που χρησιμοποιούν.In the context of this thesis we studied the notion of trust in various networking sites, as well as the types of trust models that have developed over the years in different networks and deal with the propagation of trust. We analyzed the various methods and place them under a unique modeling framework that follows the formalisms and main concepts of a path algebra formalism. In our experiments we chose three algorithms that model trust differently and implemented them both in their original form and in their corresponding variations that we have proposed. Given a sample of the Epinions network, a comparison is made on the running speed of these algorithms. Furthermore, those models were adopted to perform a survey with researchers working at the Department of Informatics and Telecommunications in NKUA. In both networks we performed experiments that deduced the average prediction error so as to compare the models in the qualitative of the predictions that their propagation mechanisms produce

An Overview of Search Strategies in Distributed Environments

[EN] Distributed systems are populated by a large number of heterogeneous entities that join and leave the systems dynamically. These entities act as clients and providers and interact with each other in order to get a resource or to achieve a goal. To facilitate the collaboration between entities the system should provide mechanisms to manage the information about which entities or resources are available in the system at a certain moment, as well as how to locate them in an e cient way. However, this is not an easy task in open and dynamic environments where there are changes in the available resources and global information is not always available. In this paper, we present a comprehensive vision of search in distributed environments. This review does not only considers the approaches of the Peer-to-Peer area, but also the approaches from three more areas: Service-Oriented Environments, Multi-Agent Systems, and Complex Networks. In these areas, the search for resources, services, or entities plays a key role for the proper performance of the systems built on them. The aim of this analysis is to compare approaches from these areas taking into account the underlying system structure and the algorithms or strategies that participate in the search process.Work partially supported by the Spanish Ministry of Science and Innovation through grants TIN2009-13839-C03-01, CSD2007-0022 (CONSOLIDER-INGENIO 2010), PROMETEO 2008/051, PAID-06-11-2048, and FPU grant AP-2008-00601 awarded to E. del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Botti, V. (2013). An Overview of Search Strategies in Distributed Environments. Knowledge Engineering Review. 1-33. https://doi.org/10.1017/S0269888913000143S133Sigdel K. , Bertels K. , Pourebrahimi B. , Vassiliadis S. , Shuai L. 2005. A framework for adaptive matchmaking in distributed computing. In Proceedings of GRID Workshop.Prabhu S. 2007. Towards distributed dynamic web service composition. In ISADS '07: Proceedings of the 8th International Symposium on Autonomous Decentralized Systems. IEEE Computer Society, 25–32.Meshkova, E., Riihijärvi, J., Petrova, M., & Mähönen, P. (2008). A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks. Computer Networks, 52(11), 2097-2128. doi:10.1016/j.comnet.2008.03.006Martin D. , Paolucci M. , Wagner M. 2007. Towards semantic annotations of web services: Owl-s from the sawsdl perspective. In Proceedings of Workshop OWL-S: Experiences and Directions at 4th European Semantic Web Conference, Innsbruck, Austria.Ogston E. , Vassiliadis S. 2001b. Matchmaking among minimal agents without a facilitator. In Proceedings of the 5th International Conference on Autonomous Agents, Bologna, Italy, 608–615.Martin D. , Burstein M. , Hobbs J. , Lassila O. , McDermott D. , McIlraith S. , Narayanan S. , Paolucci M. , Parsia B. , Payne T. , Sirin E. , Srinivasan N. , Sycara K. 2004. Owl-s: Semantic Markup for Web Services. http://www.w3.org/Submission/OWL-S/Eng Keong Lua, Crowcroft, J., Pias, M., Sharma, R., & Lim, S. (2005). A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys & Tutorials, 7(2), 72-93. doi:10.1109/comst.2005.1610546Liang J. , Kumar R. , Ross K. 2005. Understanding kazaa. In Proceedings of the 5th New York Metro Area Networking Workshop (NYMAN), New York, USA.Ko, S. Y., Gupta, I., & Jo, Y. (2008). A new class of nature-inspired algorithms for self-adaptive peer-to-peer computing. ACM Transactions on Autonomous and Adaptive Systems, 3(3), 1-34. doi:10.1145/1380422.1380426Kleinberg J. 2001. Small-world phenomena and the dynamics of information. In Advances in Neural Information Processing Systems (NIPS), Dietterich, T. G., Becker, S. & Ghahramani, Z. (eds). MIT Press, 431–438.Jha S. , Chalasani P. , Shehory O. , Sycara K. 1998. A formal treatment of distributed matchmaking. In Proceedings of the 2nd International Conference on Autonomous Agents, Sycara, K. P. & Wooldridge, M. (eds). ACM, 457–458.Huhns, M. N. (2002). Agents as Web services. IEEE Internet Computing, 6(4), 93-95. doi:10.1109/mic.2002.1020332He Q. , Yan J. , Yang Y. , Kowalczyk R. , Jin H. 2008. Chord4s: A p2p-based decentralised service discovery approach. In IEEE International Conference on Services Computing, Honolulu, Hawaii, USA, 1, 221–228.Lv Q. , Cao P. , Cohen E. , Li K. , Shenker S. 2002. Search and replication in unstructured peer-to-peer networks. In Proceedings of the 16th International Conference on Supercomputing, ICS '02. ACM, 84–95.Maymounkov P. , Mazieres D. 2002. Kademlia: a peer-to-peer information system based on the xor metric. Proceedings of the 1st International Workshop on Peer-to Peer Systems (IPTPS02), Cambridge, MA, USA.Stoica, I., Morris, R., Karger, D., Kaashoek, M. F., & Balakrishnan, H. (2001). Chord. ACM SIGCOMM Computer Communication Review, 31(4), 149-160. doi:10.1145/964723.383071Fernández A. , Ossowski S. , Vasirani M. 2008. General Architecture. CASCOM: Intelligent Service Coordination in the Semantic Web. Whitestein Series in Software Agent Technologies and Autonomic Computing, 143–160.Ding D. , Liu L. , Schmeck H. 2010. Service discovery in self-organizing service-oriented environments. In Proceedings of the 2010 IEEE Asia-Pacific Services Computing Conference. IEEE Computer Society, 717–724.Crespo A. , Garcia-Molina H. 2004. Semantic overlay networks for p2p systems. In Proceedings of the 3rd International Workshop on Agents and Peer-to-Peer Computing, Lecture Notes in Computer Science, 3601, 1–13. Springer.Rao J. , Su X. 2004. A survey of automated web service composition methods. In Proceedings of the 1st International Workshop on Semantic Web Services and Web Process Composition, SWSWPC 2004, San Diego, CA, USA, 43–54.Constantinescu I. , Faltings B. 2003. Efficient matchmaking and directory services. In Web Intelligence. IEEE Computer Society, 75–81.Cong Z. , Fernández A. 2010. Behavioral matchmaking of semantic web services. In Proceedings of the 4th International Joint Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (SMR2), Karlsruhe, Germany, 667, 131–140.Cholvi V. , Rodero-Merino L. 2007. Using random walks to find resources in unstructured self-organized p2p networks. In Proceedings of the IEEE Workshop on Dependable Application Support in Self-Organizing Networks, Edinburgh, UK, 51–56.Vázquez-Salceda J. , Vasconcelos W. W. , Padget J. , Dignum F. , Clarke S. , Roig M. P. 2010. Alive: an agent-based framework for dynamic and robust service-oriented applications. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1, AAMAS '10, International Foundation for Autonomous Agents and Multiagent Systems, 1637–1638.Liu L. , Schmeck H. 2010. Enabling self-organising service level management with automated negotiation. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT '10, Huang, J. X., Ghorbani, A. A., Hacid, M.-S. & Yamaguchi, T. (eds). IEEE Computer Society, 42–45.Campo C. , Martin A. , Garcia C. , Breuer P. 2002. Service discovery in pervasive multi-agent systems. In AAMAS Workshop on Ubiquitous Agents on Embedded, Wearable, and Mobile Agents, Bologna, Italy.Brazier, F. M. T., Kephart, J. O., Parunak, H. V. D., & Huhns, M. N. (2009). Agents and Service-Oriented Computing for Autonomic Computing: A Research Agenda. IEEE Internet Computing, 13(3), 82-87. doi:10.1109/mic.2009.51Bisnik N. , Abouzeid A. 2005. Modeling and analysis of random walk search algorithms in p2p networks. In Proceedings of the 2nd International Workshop on Hot Topics in Peer-to-Peer Systems, Anglano, C. & Mancini, L. V. (eds). IEEE Computer Society, 95–103.Huhns, M. N., Singh, M. P., Burstein, M., Decker, K., Durfee, E., Finin, T., … Zavala, L. (2005). Research Directions for Service-Oriented Multiagent Systems. IEEE Internet Computing, 9(6), 65-70. doi:10.1109/mic.2005.132Ben-Ami D. , Shehory O. 2005. A comparative evaluation of agent location mechanisms in large scale mas. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS '05, Pechoucek, M., Steiner, D. & Thompson, S. (eds). ACM, 339–346.Basters U. , Klusch M. 2006. Rs2d: Fast adaptive search for semantic web services in unstructured p2p networks. In International Semantic Web Conference, Lecture Notes in Computer Science 4273, 87–100. Springer.Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Liu G. , Wang Y. , Orgun M. 2010. Optimal social trust path selection in complex social networks. In Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI). AAAI Press, 1391–1398.Adamic, L., & Adar, E. (2005). How to search a social network. Social Networks, 27(3), 187-203. doi:10.1016/j.socnet.2005.01.007Kalogeraki V. , Gunopulos D. , Zeinalipour-Yazti D. 2002. A local search mechanism for peer-to-peer networks. In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM '02). ACM, 300–307.Babaoglu O. , Meling H. , Montresor A. 2002. Anthill: a framework for the development of agent-based peer-to-peer systems. In Proceedings of the 22nd International Conference on Distributed Computing Systems, Vienna, Austria, 15–22.Yang B. , Garcia-Molina H. 2002. Efficient search in peer-to-peer networks. In Proceedings of the International Conference on Distributed Computing Systems (ICDCS).Mokhtar S. , Kaul A. , Georgantas N. , Issarny V. 2006. Towards efficient matching of semantic web service capabilities. In Proceedings of International Workshop on Web Services – Modeling and Testing, Palermo, Italy.Fernández A. , Vasirani M. , Cáceres C. , Ossowski S. 2006. Role-based service description and discovery. In AAMAS-06 Workshop on Service-Oriented Computing and Agent-Based Engineering, 1–14.Bailey J. 2006. Fast discovery of interesting collections of web services. In WI '06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Computer Society, 152–160.Rowstron A. I. T. , Druschel P. 2001. Pastry: scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In Proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms Heidelberg, Middleware '01, Sventek, J. & Coulson, G. (eds). Springer-Verlag, 329–350.Kleinberg J. 2006. Complex networks and decentralized search algorithms. In Proceedings of the International Congress of Mathematicians (ICM), Madrid, Spain.Bachlechner D. , Siorpaes K. , Fensel D. , Toma I. 2006. Web service discovery – a reality check. In Proceedings of the 3rd European Semantic Web Conference, Seoul, South Korea.Lopes, A. L., & Botelho, L. M. (2008). Improving Multi-Agent Based Resource Coordination in Peer-to-Peer Networks. Journal of Networks, 3(2). doi:10.4304/jnw.3.2.38-47Klusch M. , Fries B. , Sycara K. 2006. Automated semantic web service discovery with owls-mx. In Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS '06, Nakashima, H., Wellman, M. P., Weiss, G. & Stone, P. (eds). ACM, 915–922.Ogston E. , Vassiliadis S. 2001a. Local distributed agent matchmaking. In Proceedings of the 9th International Conference on Cooperative Information Systems, Trento, Italy.Nguyen V. , Martel C. 2005. Analyzing and characterizing small-world graphs. In SODA '05: Proceedings of the Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics.Amaral, L. A. N., & Ottino, J. M. (2004). Complex networks. The European Physical Journal B - Condensed Matter, 38(2), 147-162. doi:10.1140/epjb/e2004-00110-5Crespo A. , Garcia-Molina H. 2002. Routing Indices For Peer-to-Peer Systems. In Proceedings of the 22nd International Conference on Distributed Computing Systems (ICDCS'02). IEEE Computer Society, 23.Manku G. S. , Bawa M. , Raghavan P. , Inc V. 2003. Symphony: Distributed hashing in a small world. In Proceedings of the 4th USENIX Symposium on Internet Technologies and Systems, Seattle, USA, 127–140.Chawathe Y. , Ratnasamy S. , Breslau L. , Lanham N. , Shenker S. 2003. Making gnutella-like p2p systems scalable. In Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM '03, Feldmann, A., Zitterbart, M., Crowcroft, J. & Wetherall, D. (eds). ACM, 407–418.Yu S. , Liu J. , Le J. 2004. Decentralized web service organization combining semantic web and peer to peer computing. In ECOWS, Lecture Notes in Computer Science 3250, 116–127. Springer.Chaari S. , Badr Y. , Biennier F. 2008. Enhancing web service selection by qos-based ontology and ws-policy. In Proceedings of the 2008 ACM Symposium on Applied Computing, SAC '08, Wainwright, R. L. & Haddad, H. (eds). ACM, 2426–2431.Michlmayr E. 2006. Ant algorithms for search in unstructured peer-to-peer networks. In Proceedings of the 22nd International Conference on Data Engineering (ICDE), Atlanta, GA, USA.Perryea C. , Chung S. 2006. Community-based service discovery. In Proceedings of the International Conference on Web Services, Chicago, IL, USA, 903–906.Upadrashta Y. , Vassileva J. , Grassmann W. 2005. Social networks in peer-to-peer systems. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii, USA.Satyanarayanan, M. (2001). Pervasive computing: vision and challenges. IEEE Personal Communications, 8(4), 10-17. doi:10.1109/98.943998Kota R. , Gibbins N. , Jennings N. R 2009. Self-organising agent organisations. In Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems – Volume 2, AAMAS '09. International Foundation for Autonomous Agents and Multiagent Systems, 797–804.Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406(6798), 845-845. doi:10.1038/35022643Watts, D. J. (2004). The «New» Science of Networks. Annual Review of Sociology, 30(1), 243-270. doi:10.1146/annurev.soc.30.020404.104342Risson, J., & Moors, T. (2006). Survey of research towards robust peer-to-peer networks: Search methods. Computer Networks, 50(17), 3485-3521. doi:10.1016/j.comnet.2006.02.001PAPAZOGLOU, M. P., TRAVERSO, P., DUSTDAR, S., & LEYMANN, F. (2008). SERVICE-ORIENTED COMPUTING: A RESEARCH ROADMAP. International Journal of Cooperative Information Systems, 17(02), 223-255. doi:10.1142/s0218843008001816Shvaiko P. , Euzenat J. 2008. Ten challenges for ontology matching. In On the Move to Meaningful Internet Systems: OTM 2008, Meersman, R. & Tari, Z. (eds), Lecture Notes in Computer Science 5332, 1164–1182. Springer.BOCCALETTI, S., LATORA, V., MORENO, Y., CHAVEZ, M., & HWANG, D. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4-5), 175-308. doi:10.1016/j.physrep.2005.10.009Bianchini D. , Antonellis V. D. , Melchiori M. 2009. Service-based semantic search in p2p systems. In Proceedings of the 2009 Seventh IEEE European Conference on Web Services, ECOWS '09, Eshuis, R., Grefen, P. & Papadopoulos, G. A. (eds). IEEE Computer Society, 7–16.Bromuri S. , Urovi V. , Morge M. , Stathis K. , Toni F. 2009. A multi-agent system for service discovery, selection and negotiation. In Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems, Sierra, C. & Castelfranchi, C. (eds). International Foundation for Autonomous Agents and Multiagent Systems, 1395–1396.Gummadi, P. K., Saroiu, S., & Gribble, S. D. (2002). A measurement study of Napster and Gnutella as examples of peer-to-peer file sharing systems. ACM SIGCOMM Computer Communication Review, 32(1), 82. doi:10.1145/510726.510756Tsoumakos D. , Roussopoulos N. 2003. Adaptive probabilistic search for peer-to-peer networks. In Peer-to-Peer Computing, Linköping, Sweeden, 102–109.Schmidt, C., & Parashar, M. (2004). A Peer-to-Peer Approach to Web Service Discovery. World Wide Web, 7(2), 211-229. doi:10.1023/b:wwwj.0000017210.55153.3dDimakopoulos V. V. , Pitoura E. 2003. A peer-to-peer approach to resource discovery in multi-agent systems. In Proceedings of Cooperative Information Agents, Lecture Notes in Computer Science 2782, 62–77. Springer.Skoutas D. , Sacharidis D. , Kantere V. , Sellis T. 2008. Efficient semantic web service discovery in centralized and p2p environments. In The Semantic Web – ISWC 2008, Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T. & Thirunarayan, K. (eds), Lecture Notes in Computer Science 5318, 583–598. Springer-Verlag.Val E. D. , Rebollo M. 2007. Service Discovery and Composition in Multiagent Systems. In Proceedings of 5th European Workshop On Multi-Agent Systems (EUMAS 2007). Association Tunisienne D'Intelligence Artificielle, 197–212.Srinivasan N. , Paolucci M. , Sycara K. 2004. Adding owl-s to uddi, implementation and throughput. In First International Workshop on Semantic Web Services and Web Process Composition (SWSWPC 2004), San Diego, CA, USA.Thadakamalla, H. P., Albert, R., & Kumara, S. R. T. (2007). Search in spatial scale-free networks. New Journal of Physics, 9(6), 190-190. doi:10.1088/1367-2630/9/6/190Papazoglou, M. P., Traverso, P., Dustdar, S., & Leymann, F. (2007). Service-Oriented Computing: State of the Art and Research Challenges. Computer, 40(11), 38-45. doi:10.1109/mc.2007.400Travers, J., & Milgram, S. (1969). An Experimental Study of the Small World Problem. Sociometry, 32(4), 425. doi:10.2307/2786545Val E. D. , Rebollo M. , Botti V. 2011. Introducing homophily to improve semantic service search in a self-adaptive system. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Taipei, Taiwan.Xiao Fan Wang, & Guanrong Chen. (2003). Complex networks: Small-world, scale-free and beyond. IEEE Circuits and Systems Magazine, 3(1), 6-20. doi:10.1109/mcas.2003.1228503Argente, E., Botti, V., Carrascosa, C., Giret, A., Julian, V., & Rebollo, M. (2010). An abstract architecture for virtual organizations: The THOMAS approach. Knowledge and Information Systems, 29(2), 379-403. doi:10.1007/s10115-010-0349-1Watts, D. J. (2002). Identity and Search in Social Networks. Science, 296(5571), 1302-1305. doi:10.1126/science.1070120Simsek Ö. , Jensen D. 2005. Decentralized search in networks using homophily and degree disparity. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, UK, 304–310.Vanthournout, K., Deconinck, G., & Belmans, R. (2005). A taxonomy for resource discovery. Personal and Ubiquitous Computing, 9(2), 81-89. doi:10.1007/s00779-004-0312-9Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440-442. doi:10.1038/30918Wei, Y., & Blake, M. B. (2010). Service-Oriented Computing and Cloud Computing: Challenges and Opportunities. IEEE Internet Computing, 14(6), 72-75. doi:10.1109/mic.2010.147Weyns, D., & Georgeff, M. (2010). Self-Adaptation Using Multiagent Systems. IEEE Software, 27(1), 86-91. doi:10.1109/ms.2010.18Pirró G. , Trunfio P. , Talia D. , Missier P. , Goble C. 2010. Ergot: a semantic-based system for service discovery in distributed infrastructures. In Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), Melbourne, Australia, 263–272.Yang B. , Garcia-Molina H. 2003. Designing a super-peer network. International Conference on Data Engineering, Bangalore, India, 49.Zhang H. , Croft W. B. , Levine B. , Lesser V. 2004a. A multi-agent approach for peer-to-peer based information retrieval system. In Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems – Volume 1, AAMAS '04. IEEE Computer Society, 456–463.Zhang, H., Goel, A., & Govindan, R. (2004). Using the small-world model to improve Freenet performance. Computer Networks, 46(4), 555-574. doi:10.1016/j.comnet.2004.05.004Sycara, K., Paolucci, M., Soudry, J., & Srinivasan, N. (2004). Dynamic discovery and coordination of agent-based semantic web services. IEEE Internet Computing, 8(3), 66-73. doi:10.1109/mic.2004.1297276Dell'Amico M. 2006. Highly clustered networks with preferential attachment to close nodes. In Proceedings of the European Conference on Complex Systems 2006, Oxford, UK.Mullender, S. J., & Vitányi, P. M. B. (1988). Distributed match-making. Algorithmica, 3(1-4), 367-391. doi:10.1007/bf01762123McIlraith, S. A., Son, T. C., & Honglei Zeng. (2001). Semantic Web services. IEEE Intelligent Systems, 16(2), 46-53. doi:10.1109/5254.920599Gkantsidis, C., Mihail, M., & Saberi, A. (2006). Random walks in peer-to-peer networks: Algorithms and evaluation. Performance Evaluation, 63(3), 241-263. doi:10.1016/j.peva.2005.01.002Zhong M. 2006. Popularity-biased random walks for peer-to-peer search under the square-root principle. In Proceedings of the 5th International Workshop on Peer-to-Peer Systems (IPTPS), Santa Barbara, CA, USA.Cao J. , Yao Y. , Zheng X. , Liu B. 2010. Semantic-based self-organizing mechanism for service registry and discovery. In Proceedings of the 14th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Shanghai, China, 345–350.Ratnasamy S. , Francis P. , Handley M. , Karp R. , Shenker S. 2001. A scalable content-addressable network. In Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '01), Cruz, R. & Varghese, G. (eds). ACM.Ouksel A. , Babad Y. , Tesch T. 2004. Matchmaking software agents in b2b markets. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04), Big Island, Hawaii, USA.Slivkins A. 2005. Distance estimation and object