TY - JOUR AU - Tibély, Gergely AU - Schrempf, Dominik AU - Derényi, Imre AU - Szöllősi, Gergely TI - Distinguishing excess mutations and increased cell death based on variant allele frequencies JF - PLOS COMPUTATIONAL BIOLOGY J2 - PLOS COMPUT BIOL VL - 18 PY - 2022 IS - 4 SN - 1553-734X DO - 10.1371/journal.pcbi.1010048 UR - https://m2.mtmt.hu/api/publication/32826209 ID - 32826209 LA - English DB - MTMT ER - TY - CHAP AU - Sasvári, Péter László AU - Urbanovics, Anna AU - Tibély, Gergely AU - Palla, Gergely ED - Auer, Ádám ED - Joó, Tamás TI - Társadalomtudományi doktori iskolák társpublikációs hálózatának elemzése T2 - Hálózatok a közszolgálatban PB - Dialóg Campus Kiadó CY - Budapest SN - 9789635310814 PY - 2019 SP - 175 EP - 204 PG - 30 DO - 10.13140/RG.2.2.15619.35367 UR - https://m2.mtmt.hu/api/publication/30947340 ID - 30947340 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Tibély, Gergely AU - Sousa-Rodrigues, D AU - Pollner, Péter AU - Palla, Gergely TI - Comparing the Hierarchy of Keywords in On-Line News Portals JF - PLOS ONE J2 - PLOS ONE VL - 11 PY - 2016 IS - 11 PG - 15 SN - 1932-6203 DO - 10.1371/journal.pone.0165728 UR - https://m2.mtmt.hu/api/publication/3142981 ID - 3142981 N1 - Department of Biological Physics, Eötvös University, Budapest, Hungary Design Group, Faculty of Maths, Computing and Technology, Open University, Walton Hall, Milton Keynes, United Kingdom MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary Cited By :1 Export Date: 8 September 2020 CODEN: POLNC AB - Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence- based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals. LA - English DB - MTMT ER - TY - JOUR AU - Tibély, Gergely AU - Pollner, Péter AU - Palla, Gergely TI - Comparing the hierarchy of author given tags and repository given tags in a large document archive JF - EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS J2 - EUR PHYS J-SPEC TOP VL - 225 PY - 2016 IS - 10 SP - 2025 EP - 2032 PG - 8 SN - 1951-6355 DO - 10.1140/epjst/e2015-50154-9 UR - https://m2.mtmt.hu/api/publication/3133887 ID - 3133887 N1 - Dept. of Biological Physics, E�tv�s University, Budapest, 1117, Hungary MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, 1117, Hungary Export Date: 16 December 2022 Correspondence Address: Tib�ly, G.; Dept. of Biological Physics, Hungary; email: tibelyg@hal.elte.hu AB - Folksonomies – large databases arising from collaborative tagging of items by independent users - are becoming an increasingly important way of categorizing information. In these systems users can tag items with free words, resulting in a tripartite item-tag-user network. Although there are no prescribed relations between tags, the way users think about the different categories presumably has some built in hierarchy, in which more special concepts are descendants of some more general categories. Several applications would benefit from the knowledge of this hierarchy. Here we apply a recent method to check the differences and similarities of hierarchies resulting from tags given by independent individuals and from tags given by a centrally managed repository system. The results from our method showed substantial differences between the lower part of the hierarchies, and in contrast, a relatively high similarity at the top of the hierarchies. © 2016, EDP Sciences and Springer. LA - English DB - MTMT ER - TY - GEN AU - Tibély, Gergely AU - Pollner, Péter AU - Palla, Gergely TI - Partial order similarity based on mutual information PY - 2016 UR - https://m2.mtmt.hu/api/publication/3042138 ID - 3042138 LA - English DB - MTMT ER - TY - JOUR AU - Palla, Gergely AU - Tibély, Gergely AU - Mones, Enys AU - Pollner, Péter AU - Vicsek, Tamás TI - Hierarchical networks of scientific journals JF - PALGRAVE COMMUNICATIONS J2 - PALGR COMMUN VL - 1 PY - 2015 SP - - PG - 9 SN - 2055-1045 DO - 10.1057/palcomms.2015.16 UR - https://m2.mtmt.hu/api/publication/2934823 ID - 2934823 N1 - MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary Regional Knowledge Centre, Eötvös University, Budapest, Hungary Department of Biological Physics, Eötvös University, Budapest, Hungary Cited By :9 Export Date: 8 September 2020 Correspondence Address: Palla, G.; MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of SciencesHungary; email: pallag@hal.elte.hu AB - Academic journals are the repositories of mankind’s gradually accumulating knowledge of the surrounding world. Just as knowledge is organized into classes ranging from major disciplines, subjects and fields, to increasingly specific topics, journals can also be categorized into groups using various metric. In addition, they can be ranked according to their overall influence. However, according to recent studies, the impact, prestige and novelty of journals cannot be characterized by a single parameter such as, for example, the impact factor. To increase understanding of journal impact, the knowledge gap we set out to explore in our study is the evaluation of journal relevance using complex multi-dimensional measures. Thus, for the first time, our objective is to organize journals into multiple hierarchies based on citation data. The two approaches we use are designed to address this problem from different perspectives. We use a measure related to the notion of m- reaching centrality and find a network that shows a journal’s level of influence in terms of the direction and efficiency with which information spreads through the network. We find we can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied to the same data. In this case, in a self-organized way, the journals become branches according to the major scientific fields, where the local structure of the branches reflect the hierarchy within the given field, with usually the most prominent journal (according to other measures) in the field chosen by the algorithm as the local root, and more specialized journals positioned deeper in the branch. This can make the navigation within different scientific fields and sub- fields very simple, and equivalent to navigating in the different branches of the nested hierarchy. We expect this to be particularly helpful, for example, when choosing the most appropriate journal for a given manuscript. According to our results, the two alternative hierarchies show a somewhat different, but also consistent, picture of the intricate relations between scientific journals, and, as such, they also provide a new perspective on how scientific knowledge is organized into networks. LA - English DB - MTMT ER - TY - JOUR AU - Tibély, Gergely AU - Pollner, Péter AU - Vicsek, Tamás AU - Palla, Gergely TI - Extracting tag hierarchies. JF - PLOS ONE J2 - PLOS ONE VL - 8 PY - 2013 IS - 12 PG - 12 SN - 1932-6203 DO - 10.1371/journal.pone.0084133 UR - https://m2.mtmt.hu/api/publication/2511816 ID - 2511816 N1 - Dept. of Biological Physics, Eötvös University, Budapest, Hungary Statistical and Biological Physics Research Group of HAS, Budapest, Hungary Eötvös University, Regional Knowledge Centre, Székesfehervár, Hungary Cited By :23 Export Date: 16 December 2022 CODEN: POLNC Correspondence Address: Dept. of Biological Physics, , Budapest, Hungary AB - Tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity. Over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a "flat", egalitarian organization of the tags, which is very common when the tags correspond to free words given by numerous independent people. Here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics. Along with proposing new algorithms, we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects. Furthermore, we set up a synthetic, computer generated benchmark providing a versatile tool for testing, with a couple of tunable parameters capable of generating a wide range of test beds. Beside the computer generated input we also use real data in our studies, including a biological example with a pre-defined hierarchy between the tags. The encouraging similarity between the pre-defined and reconstructed hierarchy, as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications. Tags have become very prevalent nowadays in various online platforms ranging from blogs through scientific publications to protein databases. Furthermore, tagging systems dedicated for voluntary tagging of photos, films, books, etc. with free words are also becoming popular. The emerging large collections of tags associated with different objects are often referred to as folksonomies, highlighting their collaborative origin and the "flat" organization of the tags opposed to traditional hierarchical categorization. Adding a tag hierarchy corresponding to a given folksonomy can very effectively help narrowing or broadening the scope of search. Moreover, recommendation systems could also benefit from a tag hierarchy. LA - English DB - MTMT ER - TY - JOUR AU - Tibély, Gergely TI - Criterions for locally dense subgraphs JF - PHYSICA A - STATISTICAL MECHANICS AND ITS APPLICATIONS J2 - PHYSICA A VL - 391 PY - 2012 IS - 4 SP - 1831 EP - 1847 PG - 17 SN - 0378-4371 DO - 10.1016/j.physa.2011.09.040 UR - https://m2.mtmt.hu/api/publication/2670572 ID - 2670572 LA - English DB - MTMT ER - TY - JOUR AU - Tibély, Gergely AU - Pollner, Péter AU - Vicsek, Tamás AU - Palla, Gergely TI - Ontologies and tag-statistics JF - NEW JOURNAL OF PHYSICS J2 - NEW J PHYS VL - 14 PY - 2012 PG - 23 SN - 1367-2630 DO - 10.1088/1367-2630/14/5/053009 UR - https://m2.mtmt.hu/api/publication/1956396 ID - 1956396 N1 - Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, 1117 Budapest, Hungary Statistical and Biological Physics Research Group of HAS, Pázmány P. stny. 1A, 1117 Budapest, Hungary Cited By :5 Export Date: 11 May 2023 Correspondence Address: Palla, G.; Statistical and Biological Physics Research Group of HAS, Pázmány P. stny. 1A, 1117 Budapest, Hungary; email: pallag@hal.elte.hu LA - English DB - MTMT ER - TY - JOUR AU - Tibély, Gergely AU - Lauri, Kovanen AU - Karsai, Márton AU - Kimmo, Kaski AU - Kertész, János AU - Jari, Saramäki TI - Communities and beyond: Mesoscopic analysis of a large social network with complementary methods JF - PHYSICAL REVIEW E - STATISTICAL, NONLINEAR AND SOFT MATTER PHYSICS (2001-2015) J2 - PHYS REV E STAT NONLIN VL - 83 PY - 2011 IS - 5 PG - 13 SN - 1539-3755 DO - 10.1103/PhysRevE.83.056125 UR - https://m2.mtmt.hu/api/publication/2670571 ID - 2670571 N1 - Institute of Physics, HAS-BME Condensed Matter Group, BME, Budapest, Budafoki út 8., H-1111, Hungary BECS, Aalto University, P.O. Box 12200, FI-00076 Aalto, Finland Cited By :22 Export Date: 25 May 2022 CODEN: PLEEE Correspondence Address: Tibély, G.; Institute of Physics, , Budapest, Budafoki út 8., H-1111, Hungary Funding details: Seventh Framework Programme, FP7, 238597 AB - Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures. LA - English DB - MTMT ER -