@inproceedings{MTMT:33698781,
	title = {Random Walk for Generalization in Goal-Directed Human Navigation on Wikipedia},
	url = {https://m2.mtmt.hu/api/publication/33698781},
	author = {Ficzere, Dániel and Hollósi, Gergely László and Frankó, Attila Ernő and Gulyás, András},
	booktitle = {Complex Networks and Their Applications XI},
	doi = {10.1007/978-3-031-21127-0_17},
	unique-id = {33698781},
	year = {2023},
	pages = {202-213}
}

@mastersthesis{MTMT:33665650,
	title = {A Function-Structure Approach to Complex Networks. Funkció és Struktúra Összefüggése Komplex Hálózatokban},
	url = {https://m2.mtmt.hu/api/publication/33665650},
	author = {Gulyás, András},
	unique-id = {33665650},
	year = {2022}
}

@inproceedings{MTMT:33031616,
	title = {Analysis of Routing Entropy in Hyperbolic Trees},
	url = {https://m2.mtmt.hu/api/publication/33031616},
	author = {Heszberger, Zalán and Majdán, András and Gulyás, András and Biro, A. and Balazs, L. and Bíró, József},
	booktitle = {2021 International Conference on Computational Science and Computational Intelligence (CSCI)},
	doi = {10.1109/CSCI54926.2021.00161},
	unique-id = {33031616},
	abstract = {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.},
	year = {2021},
	pages = {587-591},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@{MTMT:32495316,
	title = {Greedy Navigational Cores in the Human Brain},
	url = {https://m2.mtmt.hu/api/publication/32495316},
	author = {Heszberger, Zalán and Majdán, András and András, Biró and Gulyás, András and László, Balázs and Vilmos, Németh and Bíró, József},
	booktitle = {Transactions on Computational Science and Computational Intelligence},
	doi = {10.1007/978-3-030-69984-0},
	unique-id = {32495316},
	year = {2021},
	pages = {337-346},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@inproceedings{MTMT:32651176,
	title = {Hyperbolic Trees in Complex Networks},
	url = {https://m2.mtmt.hu/api/publication/32651176},
	author = {Heszberger, Zalán and Gulyás, András and Biro, A. and Majdán, András and Balazs, L. and Bíró, József},
	booktitle = {2020 International Conference on Computational Science and Computational Intelligence (CSCI)},
	doi = {10.1109/CSCI51800.2020.00254},
	unique-id = {32651176},
	abstract = {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.},
	keywords = {TOPOLOGY; complex networks; complex networks; Intelligent computing; Forestry; scale-free distribution; Metric spaces; Hyperbolic plane; Hyperbolic spaces; Generative model; Autonomous systems; Free flight; hyperbolic trees; Hyperbolic networks; Hyperbolic tree; Scale-free degree},
	year = {2020},
	pages = {1365-1371},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@inproceedings{MTMT:31797949,
	title = {Proximity in the Brain},
	url = {https://m2.mtmt.hu/api/publication/31797949},
	author = {Heszberger, Zalán and Gulyás, András and Andras, Biro and Laszlo, Balazs and Szabolcs, Mezei and Bíró, József},
	booktitle = {Proc. of 7th Annual Conf. on Computational Science & Computational Intelligence (CSCI'20)},
	doi = {10.1109/CSCI51800.2020.00256},
	unique-id = {31797949},
	year = {2020},
	pages = {111-118},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@inproceedings{MTMT:31797935,
	title = {Greedy Navigational Cores in the Human Brain},
	url = {https://m2.mtmt.hu/api/publication/31797935},
	author = {Heszberger, Zalán and Majdán, András and Andras, Biro and Gulyás, András and Laszlo, Balazs and Bíró, József},
	booktitle = {Proc. of CSCE'20 - The 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing},
	unique-id = {31797935},
	abstract = {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.},
	year = {2020},
	pages = {20-29},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@article{MTMT:31238475,
	title = {On the Memory Requirement of Hop-by-hop Routing: Tight Bounds and Optimal Address Spaces},
	url = {https://m2.mtmt.hu/api/publication/31238475},
	author = {Kőrösi, Attila and Gulyás, András and Heszberger, Zalán and Bíró, József and Rétvári, Gábor},
	doi = {10.1109/TNET.2020.2984761},
	journal-iso = {IEEE ACM T NETWORK},
	journal = {IEEE-ACM TRANSACTIONS ON NETWORKING},
	volume = {28},
	unique-id = {31238475},
	issn = {1063-6692},
	abstract = {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.},
	keywords = {compact routing, name independent and name dependent routing, information theory, routing table entropy},
	year = {2020},
	eissn = {1558-2566},
	pages = {1353-1363},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@book{MTMT:31195156,
	title = {PATHS},
	url = {https://m2.mtmt.hu/api/publication/31195156},
	isbn = {9783030475451},
	author = {Gulyás, András and Bíró, József and Heszberger, Zalán},
	doi = {10.1007/978-3-030-47545-1},
	publisher = {Springer Netherlands},
	unique-id = {31195156},
	year = {2020},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@article{MTMT:31146298,
	title = {The role of detours in individual human navigation patterns of complex networks},
	url = {https://m2.mtmt.hu/api/publication/31146298},
	author = {Gulyás, András and Bíró, József and Rétvári, Gábor and Novák, Márton and Kőrösi, Attila and Slíz, Marianna Ilona and Heszberger, Zalán},
	doi = {10.1038/s41598-020-57856-4},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {10},
	unique-id = {31146298},
	issn = {2045-2322},
	year = {2020},
	eissn = {2045-2322},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702; Slíz, Marianna Ilona/0000-0001-5959-1018}
}