2019
Vitoropoulou, M.; Karyotis, V.; Papavassiliou, S.
Sensing and monitoring of information diffusion in complex online social networks Journal Article
In: Peer-to-Peer Networking and Applications, vol. 12, no. 3, pp. 604-619, 2019, ISSN: 19366442, (cited By 6).
Abstract | Links | BibTeX | Tags: Biased random walk; Information diffusion; Information sensing; Network Monitoring; On-line social networks, Complex networks; Random processes; Topology, Social networking (online)
@article{Vitoropoulou2019604,
title = {Sensing and monitoring of information diffusion in complex online social networks},
author = {M. Vitoropoulou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054326149&doi=10.1007%2fs12083-018-0684-7&partnerID=40&md5=390e817a3dd9238e84b5acc810114175},
doi = {10.1007/s12083-018-0684-7},
issn = {19366442},
year = {2019},
date = {2019-01-01},
journal = {Peer-to-Peer Networking and Applications},
volume = {12},
number = {3},
pages = {604-619},
publisher = {Springer New York LLC},
abstract = {Sensing and monitoring information diffusion in online social networks is a complex problem of prominent importance, typically requiring significant sensing resources to address it properly. In this paper, we propose an inference approach for an information diffusion process where information is considered to belong to different classes, characterized by different spreading dynamics and possibly different topical content. Our framework utilizes social network analysis metrics in order to reduce the sensing resources that would be required in an otherwise exhaustive approach, while employing statistical learning and probabilistic inference for maintaining the accuracy of information tracking, whenever needed. The proposed framework defines an edge coloring scheme, based on which it is possible to keep track of information diffusion. We assume that the latter spreads according to various biased random walks that represent the dynamics of the considered classes of information. We have employed learning for the inference of those cases where backtracking leads to multiple potential choices for information paths. We demonstrate the operation and efficacy of our approach in characteristic online social networks, such as distributed wireless (spatial) and scale-free (relational) topologies, and draw conclusions on the impact of topology on information spreading. Finally, we discuss the emerging trends applicable for each topology and provide broader guidelines on the suitability of the proposed information diffusion inference scheme for each network. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 6},
keywords = {Biased random walk; Information diffusion; Information sensing; Network Monitoring; On-line social networks, Complex networks; Random processes; Topology, Social networking (online)},
pubstate = {published},
tppubtype = {article}
}
Sensing and monitoring information diffusion in online social networks is a complex problem of prominent importance, typically requiring significant sensing resources to address it properly. In this paper, we propose an inference approach for an information diffusion process where information is considered to belong to different classes, characterized by different spreading dynamics and possibly different topical content. Our framework utilizes social network analysis metrics in order to reduce the sensing resources that would be required in an otherwise exhaustive approach, while employing statistical learning and probabilistic inference for maintaining the accuracy of information tracking, whenever needed. The proposed framework defines an edge coloring scheme, based on which it is possible to keep track of information diffusion. We assume that the latter spreads according to various biased random walks that represent the dynamics of the considered classes of information. We have employed learning for the inference of those cases where backtracking leads to multiple potential choices for information paths. We demonstrate the operation and efficacy of our approach in characteristic online social networks, such as distributed wireless (spatial) and scale-free (relational) topologies, and draw conclusions on the impact of topology on information spreading. Finally, we discuss the emerging trends applicable for each topology and provide broader guidelines on the suitability of the proposed information diffusion inference scheme for each network. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
2018
Stai, E.; Milaiou, E.; Karyotis, V.; Papavassiliou, S.
Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 5, no. 1, pp. 256-264, 2018, ISSN: 2329924X, (cited By 40).
Abstract | Links | BibTeX | Tags: Adaptation models; Computational model; Epidemic modeling; Hashtags; Infection rates; Information propagation; Tagging; Twitter, Analytical models; Data structures; Dynamics; Epidemiology; Information dissemination; Mathematical models, Social networking (online)
@article{Stai2018256,
title = {Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation},
author = {E. Stai and E. Milaiou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040913521&doi=10.1109%2fTCSS.2017.2784184&partnerID=40&md5=7974b86c93a772693bc3d1ec0fa67f3e},
doi = {10.1109/TCSS.2017.2784184},
issn = {2329924X},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {5},
number = {1},
pages = {256-264},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type. © 2014 IEEE.},
note = {cited By 40},
keywords = {Adaptation models; Computational model; Epidemic modeling; Hashtags; Infection rates; Information propagation; Tagging; Twitter, Analytical models; Data structures; Dynamics; Epidemiology; Information dissemination; Mathematical models, Social networking (online)},
pubstate = {published},
tppubtype = {article}
}
Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type. © 2014 IEEE.
2014
Stai, E.; Karyotis, V.; Papavassiliou, S.
Exploiting socio-physical network interactions via a utility-based framework for resource management in mobile social networks Journal Article
In: IEEE Wireless Communications, vol. 21, no. 1, pp. 10-17, 2014, ISSN: 15361284, (cited By 16).
Abstract | Links | BibTeX | Tags: Infrastructure networks; Mobile social networks; Mobile telecommunications; On-line social networks; Personalized advertisings; Resource management; Utility-based framework; Wireless communications, Natural resources management; Resource allocation; Telecommunication networks; Wireless telecommunication systems, Social networking (online)
@article{Stai201410,
title = {Exploiting socio-physical network interactions via a utility-based framework for resource management in mobile social networks},
author = {E. Stai and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896446369&doi=10.1109%2fMWC.2014.6757892&partnerID=40&md5=f25c02100f1cd76420991d05f7580ad9},
doi = {10.1109/MWC.2014.6757892},
issn = {15361284},
year = {2014},
date = {2014-01-01},
journal = {IEEE Wireless Communications},
volume = {21},
number = {1},
pages = {10-17},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Mobile social networks have the lion's share in modern mobile telecommunications, and their interaction with the underlying infrastructure networks has attracted significant attention due to its impact on resource management. In this article, we present and demonstrate a framework for addressing such interplay between online social networks and wireless communications by exploiting principles from the theory of utility-based engineering and elements from social network analysis. We aim at a holistic design framework that allows the joint development of improved resource management mechanisms for future mobile wireless infrastructures and their social counterparts. We demonstrate the proposed methodology and reveal the key aspects of designing and exploiting convenient utility functions within the framework of network science in order to better manage the available resources, improve infrastructures, and eventually obtain from them the maximum possible benefit. We establish the above principles and emerging design potentials in future complex networks by presenting two tangible examples where personalized advertising and topology control in MSNs are used to exploit and validate different network and individual socio-utility maximization features, respectively. © 2014 IEEE.},
note = {cited By 16},
keywords = {Infrastructure networks; Mobile social networks; Mobile telecommunications; On-line social networks; Personalized advertisings; Resource management; Utility-based framework; Wireless communications, Natural resources management; Resource allocation; Telecommunication networks; Wireless telecommunication systems, Social networking (online)},
pubstate = {published},
tppubtype = {article}
}
Mobile social networks have the lion's share in modern mobile telecommunications, and their interaction with the underlying infrastructure networks has attracted significant attention due to its impact on resource management. In this article, we present and demonstrate a framework for addressing such interplay between online social networks and wireless communications by exploiting principles from the theory of utility-based engineering and elements from social network analysis. We aim at a holistic design framework that allows the joint development of improved resource management mechanisms for future mobile wireless infrastructures and their social counterparts. We demonstrate the proposed methodology and reveal the key aspects of designing and exploiting convenient utility functions within the framework of network science in order to better manage the available resources, improve infrastructures, and eventually obtain from them the maximum possible benefit. We establish the above principles and emerging design potentials in future complex networks by presenting two tangible examples where personalized advertising and topology control in MSNs are used to exploit and validate different network and individual socio-utility maximization features, respectively. © 2014 IEEE.