@article{thomas2023graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, interhash = {229b2eb23d7fdfdf16d3c6d813e4c106}, intrahash = {853c98d29b7c266e48e1cabd45dc5854}, journal = {Transactions on Machine Learning Research}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, url = {https://openreview.net/pdf?id=h4BYtZ79uy}, year = 2023 } @inproceedings{pereiranunes2012entities, abstract = {The richness of the (Semantic) Web lies in its ability to link related resources as well as data across the Web. However, while relations within particular datasets are often well defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as document corpora, opens up opportunities to exploit their inherent semantics to uncover semantic relationships between disparate resources. In this paper, we present an approach to uncover relationships between disparate entities by analyzing the graphs of used reference datasets. We adapt a relationship assessment methodology from social network theory to measure the connectivity between entities in reference datasets and exploit these measures to identify correlated Web resources. Finally, we present an evaluation of our approach using the publicly available datasets Bibsonomy and USAToday. }, author = {Pereira Nunes, Bernardo and Kawase, Ricardo and Dietze, Stefan and Taibi, Davide and Casanova, Marco Antonio and Nejdl, Wolfgang}, booktitle = {Proceedings of the Web of Linked Entities Workshop in conjuction with the 11th International Semantic Web Conference}, editor = {Rizzo, Giuseppe and Mendes, Pablo and Charton, Eric and Hellmann, Sebastian and Kalyanpur, Aditya}, institution = {Bernardo Pereira Nunes, Ricardo Kawase, Stefan Dietze, Davide Taibi, Marco Antonio Casanova, Wolfgang Nejdl}, interhash = {8f969b917268449792c130dcbab06e69}, intrahash = {f22943239296ada0dfa11c30c5b4904a}, issb = {1613-0073}, month = nov, pages = {45--57}, series = {CEUR-WS.org}, title = {Can Entities be Friends?}, url = {http://ceur-ws.org/Vol-906/paper6.pdf}, urn = {urn:nbn:de:0074-906-7}, volume = 906, year = 2012 } @inproceedings{DBLP:conf/dsaa/KrompassNT14, author = {Krompass, Denis and Nickel, Maximilian and Tresp, Volker}, bibsource = {dblp computer science bibliography, http://dblp.org}, booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014}, crossref = {DBLP:conf/dsaa/2014}, doi = {10.1109/DSAA.2014.7058046}, interhash = {0ca986606c22ca0b3780c9b9c25f31c7}, intrahash = {c952ed96ece470e4fa5336eedf670d5b}, isbn = {978-1-4799-6991-3}, pages = {18--24}, publisher = {{IEEE}}, title = {Large-scale factorization of type-constrained multi-relational data}, url = {http://dx.doi.org/10.1109/DSAA.2014.7058046}, year = 2014 } @article{noKey, abstract = {Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale Online-Netze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet. }, author = {Heidtmann, Klaus}, doi = {10.1007/s00287-012-0654-z}, interhash = {d1ab5ecb2270150ba3d46e156d3a1139}, intrahash = {783481b980117c756d4560c1640ae4a0}, issn = {0170-6012}, journal = {Informatik-Spektrum}, language = {German}, number = 5, pages = {440-448}, publisher = {Springer Berlin Heidelberg}, title = {Internet-Graphen}, url = {http://dx.doi.org/10.1007/s00287-012-0654-z}, volume = 36, year = 2013 } @book{janson2000theory, address = {New York; Chichester}, author = {Janson, Svante and Luczak, Tomasz and Rucinski, Andrzej}, interhash = {929294638db37c413b283ac468bbdade}, intrahash = {7bb074240f72009f515123f15afecefd}, isbn = {0471175412 9780471175414}, publisher = {John Wiley & Sons}, refid = {43340250}, title = {Theory of random graphs}, url = {http://www.amazon.com/Random-Graphs-Svante-Janson/dp/0471175412}, year = 2000 } @book{diestel2006graphentheorie, author = {Diestel, Reinhard}, edition = {3 (electronic edition)}, interhash = {f2579f4c24fdf2233f0a0565b34e8ac1}, intrahash = {bf75f61d316d1d149e2b7e0d72cd937c}, pages = {I-XVI, 1-344}, publisher = {Springer-Verlag Heidelberg, New York}, title = {Graph Theory}, url = {http://www.math.ubc.ca/~solymosi/2007/443/GraphTheoryIII.pdf}, year = 2005 } @article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 } @inproceedings{doerfel2013analysis, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.}, acmid = {2507222}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, doi = {10.1145/2507157.2507222}, interhash = {3eaf2beb1cdad39b7c5735a82c3338dd}, intrahash = {aa4b3d79a362d7415aaa77625b590dfa}, isbn = {978-1-4503-2409-0}, location = {Hong Kong, China}, numpages = {4}, pages = {343--346}, publisher = {ACM}, series = {RecSys '13}, title = {An analysis of tag-recommender evaluation procedures}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf}, year = 2013 } @article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 } @article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 }