Δημοσιεύσεις

@article{Tsitseklis2021377,

title = {Socio-Aware Recommendations under Complex User Constraints},

author = {K. Tsitseklis and M. Vitoropoulou and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099574885&doi=10.1109%2fTCSS.2020.3046686&partnerID=40&md5=dd9c5d680b886d8e4fcaf5abba0c7d4e},

doi = {10.1109/TCSS.2020.3046686},

issn = {2329924X},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Computational Social Systems},

volume = {8},

number = {2},

pages = {377-387},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.},

note = {cited By 1},

keywords = {Complex networks, Constrained recommendations; Information diffusion process; Information overloads; Joint behavior; Online social networks (OSN); Two-step procedure; User constraints; Users' -constraints, Heuristic methods; Social networking (online)},

pubstate = {published},

tppubtype = {article}

}

@article{Karyotis2016227,

title = {On the evolution of complex network topology under network churn},

author = {V. Karyotis and E. Stai and S. Papavassiliou},

editor = {Mamatas L. Matta I. Papadimitriou P.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978959144&doi=10.1007%2f978-3-319-33936-8_18&partnerID=40&md5=d66eefa06bcaa59521adbf8ba126608e},

doi = {10.1007/978-3-319-33936-8_18},

issn = {03029743},

year  = {2016},

date = {2016-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {9674},

pages = {227-240},

publisher = {Springer Verlag},

abstract = {The future Internet is becoming more diverse, incorporating heterogeneous access networks. The latter are characterized by numerous devices that join/leave the network dynamically, creating intense churn patterns. New approaches to analyze and quantify churn-induced network evolution are required. In this paper, we address such need by introducing a new analysis framework that maps network evolution into trajectories in multi-dimensional vector spaces. Each network instance is characterized by a feature vector, indicating network properties of interest. To demonstrate the potentials of this approach, we exemplify and study the effect of edge churn on various complex topologies, frequently emerging in various communications environments. We investigate via simulation the impact of network evolution, by quantifying its effect on key network analysis metrics, such as the clustering coefficient and the plethora of centrality metrics, employed at large for analyzing topologies and designing applications. The proposed framework aspires to establish more holistic and efficient complex network control. © IFIP International Federation for Information Processing 2016.},

note = {cited By 1; Conference of 14th IFIP WG 6.2 International Conference on Wired/Wireless Internet Communications, WWIC 2016 ; Conference Date: 25 May 2016 Through 27 May 2016; Conference Code:176559},

keywords = {Analysis frameworks; Clustering coefficient; Edge churn; Future internet; Heterogeneous access; Multi-dimensional vectors; Network evolution; Network properties, Complex networks, Internet; Topology; Vector spaces},

pubstate = {published},

tppubtype = {article}

}

@article{Karyotis2015577,

title = {Macroscopic malware propagation dynamics for complex networks with churn},

author = {V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927730777&doi=10.1109%2fLCOMM.2015.2399925&partnerID=40&md5=70ea29f069651e438d8b63decdc8a8f6},

doi = {10.1109/LCOMM.2015.2399925},

issn = {10897798},

year  = {2015},

date = {2015-01-01},

journal = {IEEE Communications Letters},

volume = {19},

number = {4},

pages = {577-580},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In this letter, a queueing-based approach for modeling malware propagation in complex networks with churn (dynamic node variation) is introduced. The proposed methodology captures the dynamics of SIS-type malware, especially in time-varying networks, where nodes enter and leave due to their limited energy reserves, or as the outcome of malware attacks. We demonstrate the developed model in various complex networks, showing how it can be exploited for analytically quantifying network reliability and further used for increasing the robustness of the network against generic malware attacks. © 2015 IEEE.},

note = {cited By 22},

keywords = {Complex networks, Computer crime; Malware; Queueing networks; Time varying networks, Developed model; Dynamic nodes; Limited energies; Malware attacks; Malware propagation; Network reliability; Network robustness; Product forms},

pubstate = {published},

tppubtype = {article}

}