TY - CHAP AU - Ficzere, Dániel AU - Hollósi, Gergely László AU - Frankó, Attila Ernő AU - Gulyás, András ED - Cherifi, Hocine ED - Mantegna, Rosario Nunzio ED - Rocha, Luis M. ED - Cherifi, Chantal ED - Miccichè, Salvatore TI - Random Walk for Generalization in Goal-Directed Human Navigation on Wikipedia T2 - Complex Networks and Their Applications XI PB - Springer International Publishing CY - Cham SN - 9783031211270 T3 - Studies in Computational Intelligence, ISSN 1860-949X ; 1077. PY - 2023 SP - 202 EP - 213 PG - 12 DO - 10.1007/978-3-031-21127-0_17 UR - https://m2.mtmt.hu/api/publication/33698781 ID - 33698781 N1 - Correspondence Address: Ficzere, D.; Budapest University of Technology and Economics, Műegyetem rkp. 3, Hungary; email: ficzere.daniel@vik.bme.hu LA - English DB - MTMT ER - TY - THES AU - Gulyás, András TI - A Function-Structure Approach to Complex Networks. Funkció és Struktúra Összefüggése Komplex Hálózatokban TS - Funkció és Struktúra Összefüggése Komplex Hálózatokban PY - 2022 UR - https://m2.mtmt.hu/api/publication/33665650 ID - 33665650 LA - English DB - MTMT ER - TY - CHAP AU - Heszberger, Zalán AU - Majdán, András AU - Gulyás, András AU - Biro, A. AU - Balazs, L. AU - Bíró, József TI - Analysis of Routing Entropy in Hyperbolic Trees T2 - 2021 International Conference on Computational Science and Computational Intelligence (CSCI) PB - IEEE CY - Piscataway (NJ) SN - 9781665458412 PY - 2021 SP - 587 EP - 591 PG - 5 DO - 10.1109/CSCI54926.2021.00161 UR - https://m2.mtmt.hu/api/publication/33031616 ID - 33031616 N1 - Faculty of Electrical Engineering and Informatics, Department of Telecommunications and Artificial Intelligence, Muegyetem rkp. 3, Budapest, H-1111, Hungary Budapest University of Technology and Economics, Muegyetem rkp. 3, Budapest, H-1111, Hungary MTA-BME Information Systems Research Group, Muegyetem rkp. 3, Budapest, H-1111, Hungary Export Date: 28 July 2022 AB - Recent results have shown that the memory requirements of destination-based hop-by-hop routing in largescale communication networks can efficiently be estimated by the information theoretic!! entropy of the forwarding tables placed at the nodes. For calculating and analyzing the memory usage the forwarding tables are to be inferred according to the routing algorithm, then the entropy values can be established. This could be a computationally intensive task, especially in case of large networks operated along complex routing policies making the analysis hard and less tractable. In this paper we focus on a special case, when the routing is based on a spanning tree the so called hyperbolic tree. We show that the routing entropy can efficiently be computed in this case without generating the forwarding tables. Based on this computation, analytical results on routing scalability with respect to memory usage can also be derived, which confirms observations on numerical investigations. These network theoretical results will expectedly have significance in the forthcoming 5th generation (5G) and the future 6th generation (6G) complex communication systems. The representation and modelling power of hyperbolic complex networks may greatly help in mastering the complexity of rapidly expanding systems like 5G and 6G communication networks. LA - English DB - MTMT ER - TY - CHAP AU - Heszberger, Zalán AU - Majdán, András AU - András, Biró AU - Gulyás, András AU - László, Balázs AU - Vilmos, Németh AU - Bíró, József TI - Greedy Navigational Cores in the Human Brain T2 - Transactions on Computational Science and Computational Intelligence PB - Springer Netherlands CY - Zürich SN - 9783030699840 PY - 2021 SP - 337 EP - 346 PG - 10 DO - 10.1007/978-3-030-69984-0 UR - https://m2.mtmt.hu/api/publication/32495316 ID - 32495316 LA - English DB - MTMT ER - TY - CHAP AU - Heszberger, Zalán AU - Gulyás, András AU - Biro, A. AU - Majdán, András AU - Balazs, L. AU - Bíró, József ED - Hamid, R. Arabnia TI - Hyperbolic Trees in Complex Networks T2 - 2020 International Conference on Computational Science and Computational Intelligence (CSCI) PB - IEEE CY - Piscataway (NJ) SN - 9781728176246 PY - 2020 SP - 1365 EP - 1371 PG - 7 DO - 10.1109/CSCI51800.2020.00254 UR - https://m2.mtmt.hu/api/publication/32651176 ID - 32651176 N1 - American Council on Science and Education Conference code: 170944 Export Date: 7 February 2022 Funding details: Hungarian Scientific Research Fund, OTKA, FK 128233, FK17 123957, K17 124171, KH18 129589 Funding details: Innovációs és Technológiai Minisztérium Funding text 1: Thanks to OTKA FK17 123957, FK 128233, KH18 129589, K17 124171 for funding. Heszberger Zalán is also supperted by MTA Bolyai János Research Grant and UNKP-20-5 Bolyai+ Research Grant. László Balázs and András Biró are supported by the ÚNKP-20-1 New National Excellence Program of the Ministry of Innovation and Technology from the source of the national research, development and innovation fund. AB - The two-dimensional hyperbolic space turned out to be an efficient geometry for generative models of complex networks. The networks generated with this hyperbolic metric space share their basic structural properties (like small diameter or scale-free degree distribution) with several real networks. In this paper, we present a new model for generating trees in the two-dimensional hyperbolic plane. The generative model is not based on known hyperbolic network models: the trees are not inferred from the existing links of any network; instead, the hyperbolic tree is generated from scratch purely based on the hyperbolic coordinates of nodes. We show that these hyperbolic trees have scale-free degree distributions and are present to a large extent both in synthetic hyperbolic complex networks and real ones (Internet autonomous system topology, US flight network) embedded in the hyperbolic plane. © 2020 IEEE. LA - English DB - MTMT ER - TY - CHAP AU - Heszberger, Zalán AU - Gulyás, András AU - Andras, Biro AU - Laszlo, Balazs AU - Szabolcs, Mezei AU - Bíró, József TI - Proximity in the Brain T2 - Proc. of 7th Annual Conf. on Computational Science & Computational Intelligence (CSCI'20) SN - 1601325134 PY - 2020 SP - 111 PG - 7 DO - 10.1109/CSCI51800.2020.00256 UR - https://m2.mtmt.hu/api/publication/31797949 ID - 31797949 N1 - Export Date: 9 June 2022 LA - English DB - MTMT ER - TY - CHAP AU - Heszberger, Zalán AU - Majdán, András AU - Andras, Biro AU - Gulyás, András AU - Laszlo, Balazs AU - Bíró, József TI - Greedy Navigational Cores in the Human Brain T2 - Proc. of CSCE'20 - The 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing SN - 1601325126 PY - 2020 SP - 20 PG - 9 UR - https://m2.mtmt.hu/api/publication/31797935 ID - 31797935 AB - Greedy navigation/routing plays an important role in geometric routing of networks because of its locality and simplicity. This can operate in geometrically embedded networks in a distributed manner, distances are calculated based on coordinates of network nodes for choosing the next hop in the routing. Based only on node coordinates in any metric space, the Greedy Navigational Core (GNC) can be identified as the minimum set of links between these nodes which provides 100% greedy navigability. In this paper we perform results on structural greedy navigability as the level of presence of Greedy Navigational Cores in structural networks of the Human Brain. LA - English DB - MTMT ER - TY - JOUR AU - Kőrösi, Attila AU - Gulyás, András AU - Heszberger, Zalán AU - Bíró, József AU - Rétvári, Gábor TI - On the Memory Requirement of Hop-by-hop Routing: Tight Bounds and Optimal Address Spaces JF - IEEE-ACM TRANSACTIONS ON NETWORKING J2 - IEEE ACM T NETWORK VL - 28 PY - 2020 IS - 3 SP - 1353 EP - 1363 PG - 11 SN - 1063-6692 DO - 10.1109/TNET.2020.2984761 UR - https://m2.mtmt.hu/api/publication/31238475 ID - 31238475 AB - We formulate the optimal address space design problem as the task to set node addresses in order to minimize certain network-wide entropy-related measures. We derive tight space bounds for many well-known graph families and we propose a simple heuristic to find optimal address spaces for general graphs. Our evaluations suggest that in structured graphs, including most practically important network topologies, significant memory savings can be attained by forwarding table compression over our optimized address spaces. According to our knowledge, our work is the first to bridge the gap between computer network scalability and information-theory. LA - English DB - MTMT ER - TY - BOOK AU - Gulyás, András AU - Bíró, József AU - Heszberger, Zalán TI - PATHS PB - Springer Netherlands CY - Basel PY - 2020 SP - 86 SN - 9783030475444 DO - 10.1007/978-3-030-47545-1 UR - https://m2.mtmt.hu/api/publication/31195156 ID - 31195156 LA - English DB - MTMT ER - TY - JOUR AU - Gulyás, András AU - Bíró, József AU - Rétvári, Gábor AU - Novák, Márton AU - Kőrösi, Attila AU - Slíz, Marianna Ilona AU - Heszberger, Zalán TI - The role of detours in individual human navigation patterns of complex networks JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 10 PY - 2020 IS - 1 PG - 10 SN - 2045-2322 DO - 10.1038/s41598-020-57856-4 UR - https://m2.mtmt.hu/api/publication/31146298 ID - 31146298 LA - English DB - MTMT ER -